Tag Archives: news

AWS Week in Review – November 14, 2022

Post Syndicated from Steve Roberts original https://aws.amazon.com/blogs/aws/aws-week-in-review-november-14-2022/

It’s now just two weeks to AWS re:Invent in Las Vegas, and the pace is picking up, both here on the News Blog, and throughout AWS as everyone get ready for the big event! I hope you get the chance to join us, and have shared links and other information at the bottom of this post. First, though, let’s dive straight in to this week’s review of news and announcements from AWS.

Last Week’s Launches
As usual, let’s start with a summary of some launches from the last week that I want to remind you of:

New Switzerland Region – First and foremost, AWS has opened a new Region, this time in Switzerland. Check out Seb’s post here on the News Blog announcing the launch.

New AWS Resource Explorer – if you’ve ever spent time searching for specific resources in your AWS account, especially across Regions, be sure to take a look at the new AWS Resource Explorer, described in this post by Danilo. Once enabled, indexes of the resources in your account are built and maintained (you have control over which resources are indexed). Once the indexes are built, you can issue queries to more quickly arrive at the required resource without jumping between different Regions and service dashboards in the Management Console.

Amazon Lightsail domain registration and DNS autoconfiguration – Amazon Lightsail users can now take advantage of new support for registering domain names with automatic configuration of DNS records. Within the Lightsail console, you’re now able to create and register an Amazon Route 53 domain with just a few clicks. 

New models for Amazon SageMaker JumpStart – Two new state-of-the-art models have been released for Amazon SageMaker JumpStart. SageMaker JumpStart provides pretrained, open-source models covering a wide variety of problem types that help you get started with machine learning. The first new model, Bloom, can be used to complete sentences or generate long paragraphs of text in 46 different languages. The second model, Stable Diffusion, generates realistic images from given text. Find out more about the new models in this What’s New post.

Mac instances and macOS Ventura – Amazon Elastic Compute Cloud (Amazon EC2) now has support for running the latest version of macOS, Ventura (13.0), for both EC2 x86 Mac and EC2 M1 Mac instances. These instances enable you to provision and run macOS environments in the AWS Cloud, for developers creating apps for iPhone, iPad, Mac, Apple Watch, Apple TV, and Safari.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Some other news items you may want to explore:

AWS Open Source News and Updates – This blog is published each week, and Installment 135 is now available, highlighting new open-source projects, tools, and demos from the AWS community.

Upcoming AWS Events
AWS re:Invent 2022 – As I noted at the top of this post, we’re now just two weeks away from the event! Join us live in Las Vegas November 28–December 2 for keynotes, opportunities for training and certification, and over 1,500 technical sessions. If you are joining us, be sure to check out the re:Invent 2022 Attendee Guides, each curated by an AWS Hero, AWS industry team, or AWS partner.

If you can’t join us live in Las Vegas, be sure to join us online to watch the keynotes and leadership sessions. My cohosts and I on the AWS on Air show will also be livestreaming daily from the event, chatting with service teams and special guests about all the launches and other announcements. You can find us on Twitch.tv (we’ll be on the front page throughout the event), the AWS channel on LinkedIn Live, Twitter.com/awsonair, and YouTube Live.

And one final update for the event – if you’re a .NET developer, be sure to check out the XNT track in the session catalog to find details on the seven breakouts, three chalk talks, and the workshop we have available for you at the conference!

Check back next Monday for our last week in review before the start of re:Invent!

— Steve

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS.

AWS Week in Review – November 7, 2022

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-week-in-review-november-7-2022/

With three weeks to go until AWS re:Invent opens in Las Vegas, the AWS News Blog Team is hard at work creating blog posts to share the latest launches and previews with you. As usual, we have a strong mix of new services, new features, and a surprise or two.

Last Week’s Launches
Here are some launches that caught my eye last week:

Amazon SNS Data Protection and Masking – After a quick public preview, this cool feature is now generally available. It uses pattern matching, machine learning models, and content policies to help protect data at scale. You can find many different kinds of personally identifiable information (PII) and protected health information (PHI) in message bodies and either block message delivery or mask (de-identify) the sensitive data, all in real-time and on a per-topic basis. To learn more, read the blog post or the message data protection documentation.

Amazon Textract Updates – This service extracts text, handwriting, and data from any document or image. This past week we updated the AnalyzeID function so that it can now extract the machine readable zone (MRZ) on passports issued by the United States, and we added the entire OCR output to the API response. We also updated the machine learning models that power the AnalyzeDocument function, with a focus on single-character boxed forms commonly found on tax and immigration documents. Finally, we updated the AnalyzeExpense function with support for new fields and higher accuracy for existing fields, bringing the total field count to more than 40.

Another Amazon Braket Processor – Our quantum computing service now supports Aquila, a new 256-qubit quantum computer from QuEra that is based on a programmable array of neutral Rubidium atoms. According to the What’s New, Aquila supports the Analog Hamiltonian Simulation (AHS) paradigm, allowing it to solve for the static and dynamic properties of quantum systems composed of many interacting particles.

Amazon S3 on Outposts – This service now lets you use additional S3 Lifecycle rules to optimize capacity management. You can expire objects as they age or are replaced with newer versions, with control at the bucket level, or for subsets defined by prefixes, object tags, or object sizes. There’s more info in the What’s New and in the S3 documentation.

AWS CloudFormation – There were two big updates last week: support for Amazon RDS Multi-AZ deployments with two readable standbys, and better access to detailed information on failed stack instances for operations on CloudFormation StackSets.

Amazon MemoryDB for Redis – You can now use data tiering as a lower cost way to to scale your clusters up to hundreds of terabytes of capacity. This new option uses a combination of instance memory and SSD storage in each cluster node, with all data stored durably in a multi-AZ transaction log. There’s more information in the What’s New and the blog post.

Amazon EC2 – You can now remove launch permissions for Amazon Machine Images (AMIs) that are directly shared with your AWS account.

X in Y – We launched existing AWS services and instance types in additional Regions:

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Here are some additional news items that you may find interesting:

AWS Open Source News and Updates – My colleague Ricardo Sueiras highlights new open source projects, tools, and demos from the AWS Community. Read Installment 134 to see what’s going on!

New Case Study – A new AWS case study describes how Taggle (a company focused on smart water solutions in Australia) created an IoT platform that runs on AWS and uses Amazon Kinesis Data Streams to store & ingest data in real time. Using AWS allowed them to scale to accommodate 80,000 additional sensors that will roll out in 2022.

Upcoming AWS Events
re:Invent 2022 – AWS re:Invent is just three weeks away! Join us live from November 28th to December 2nd for keynotes, training and certification opportunities, and over 1,500 technical sessions. If you cannot make it to Las Vegas you can also join us online to watch the keynotes and leadership sessions live. Be sure to check out the re:Invent 2022 Attendee Guides, each curated by an AWS Hero, AWS industry team, or AWS partner.

PeerTalk – If you will be attending re:Invent in person and are interested in meeting with me or any of our featured experts, be sure to check out PeerTalk, our new onsite networking program.

That’s all for this week!

— Jeff;

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS.

AWS Week in Review – October 31, 2022

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/aws-week-in-review-october-31-2022/

No tricks, just treats in this weekly roundup of news and announcements. Let’s switch our AWS Management Console into dark mode and dive right into it.

Last Week’s Launches
Here are some launches that got my attention during the previous week:

AWS Local Zones in Hamburg and Warsaw now generally available – AWS Local Zones help you run latency-sensitive applications closer to end users. The AWS Local Zones in Hamburg, Germany, and Warsaw, Poland, are the first Local Zones in Europe. AWS Local Zones are now generally available in 20 metro areas globally, with announced plans to launch 33 additional Local Zones in metro areas around the world. See the full list of available and announced AWS Local Zones, and learn how to get started.

Amazon SageMaker multi-model endpoint (MME) now supports GPU instances – MME is a managed capability of SageMaker Inference that lets you deploy thousands of models on a single endpoint. MMEs can now run multiple models on a GPU core, share GPU instances behind an endpoint across multiple models, and dynamically load and unload models based on the incoming traffic. This can help you reduce costs and achieve better price performance. Learn how to run multiple deep learning models on GPU with Amazon SageMaker multi-model endpoints.

Amazon EC2 now lets you replace the root Amazon EBS volume for a running instance – You can now use the Replace Root Volume for patching features in Amazon EC2 to replace your instance root volume using an updated AMI without needing to stop the instance. This makes patching of the guest operating system and applications easier, while retraining the instance store data, networking, and IAM configuration. Check out the documentation to learn more.

AWS Fault Injection Simulator now supports network connectivity disruption – AWS Fault Injection Simulator (FIS) is a managed service for running controlled fault injection experiments on AWS. AWS FIS now has a new action type to disrupt network connectivity and validate that your applications are resilient to a total or partial loss of connectivity. To learn more, visit Network Actions in the AWS FIS user guide.

Amazon SageMaker Automatic Model Tuning now supports Grid Search – SageMaker Automatic Model Tuning helps you find the hyperparameter values that result in the best-performing model for a chosen metric. Until now, you could choose between random, Bayesian, and hyperband search strategies. Grid search now lets you cover every combination of the specified hyperparameter values for use cases in which you need reproducible tuning results. Learn how Amazon SageMaker Automatic Model Tuning now supports grid search.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Here are some additional news items that you may find interesting:

Celebrating over 20 years of AI/ML innovation – On October 25, we hosted the AWS AI/ML Innovation Day. Bratin Saha and other leaders in the field shared the great strides we have made in the past and discussed what’s next in the world of ML. You can watch the recording here.

AWS open-source news and updates – My colleague Ricardo Sueiras writes this weekly open-source newsletter in which he highlights new open-source projects, tools, and demos from the AWS Community. Read edition #133 here.

Upcoming AWS Events
Check your calendars and sign up for these AWS events:

AWS re:Invent is only 4 weeks away! Join us live in Las Vegas from November 28–December 2 for keynote announcements, training and certification opportunities, access to 1,500+ technical sessions, and much more. Seats are still available to reserve, and walk-ups are available onsite. You can also join us online to watch live keynotes and leadership sessions.

If you are into machine learning like me, check out the ML attendee guide. AWS Machine Learning Hero Vinicius Caridá put together recommended sessions and tips and tricks for building your agenda. We also have attendee guides on additional topics and industries.

On November 2, there is a virtual event for building modern .NET applications on AWS. You can register for free.

On November 11–12, AWS User Groups in India are hosting the AWS Community Day India 2022, with success stories, use cases, and much more from industry leaders. Sign up for free to join this virtual event.

That’s all for this week. Check back next Monday for another Week in Review!

— Antje

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

AWS Named as a Leader in the 2022 Gartner Cloud Infrastructure & Platform Services (CIPS) Magic Quadrant for the 12th Consecutive Year

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/aws-named-as-a-leader-in-the-2022-gartner-cloud-infrastructure-platform-services-cips-magic-quadrant-for-the-12th-consecutive-year/

This year, and for the twelfth consecutive year, AWS has been named as a Leader in the 2022 Magic Quadrant for Cloud Infrastructure and Platform Services (CIPS). Per Gartner, AWS is the longest-running CIPS Magic Quadrant Leader.

AWS was among the first cloud providers when we launched Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Simple Storage Service (Amazon S3) 16 years ago. Our APIs have been adopted by the whole industry and often copied by others.

We believe this report validates AWS’s ability to innovate and deliver the broadest and deepest set of services for cloud computing. I encourage you to read the full report to appreciate the details.

As Jeff Bezos wrote in his first letter to shareholders in 1997 (reprinted at the end of each annual letter since then), Amazon makes decisions and weighs trade-offs differently than some companies. We focus on the long-term value rather than short-term profits, we make bold rather than timid investment decisions, and most importantly, we relentlessly focus on you: our customers. As a matter of fact, 90 percent of AWS’s roadmap for new services and capabilities is directly driven by your feedback and requests.

I work with AWS service teams every day. These teams work hard to innovate on your behalf. They make bold investments to invent, build, and operate services that help you innovate and build amazing experiences for your customers. The entire team is proud to see these efforts recognized by Gartner.

Our teams closely work with the vibrant AWS Partner Network. AWS has the largest and most dynamic community, with millions of active customers every month and more than 100,000 partners from over 150 countries—with almost 70% headquartered outside the United States. There is a real network effect when you use AWS.

The Magic Quadrant for CIPS, showing Amazon Web Services as a leader.

The full Gartner report has details about the features and factors they reviewed. It explains the methodology used and the results. This report can serve as a guide when choosing a cloud provider that helps you innovate on behalf of your customers.

— seb

Gartner, Magic Quadrant for Cloud Infrastructure and Platform Services, 19 October 2022, Raj Bala, et. al.


The Magic Quadrant graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from AWS.
Gartner does not endorse any vendor, product or service depicted in our research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Gartner and Magic Quadrant are registered trademarks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

 

Introducing Amazon Neptune Serverless – A Fully Managed Graph Database that Adjusts Capacity for Your Workloads

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/introducing-amazon-neptune-serverless-a-fully-managed-graph-database-that-adjusts-capacity-for-your-workloads/

Amazon Neptune is a fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. With Neptune, you can use open and popular graph query languages to execute powerful queries that are easy to write and perform well on connected data. You can use Neptune for graph use cases such as recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security.

Neptune has always been fully managed and handles time-consuming tasks such as provisioning, patching, backup, recovery, failure detection and repair. However, managing database capacity for optimal cost and performance requires you to monitor and reconfigure capacity as workload characteristics change. Also, many applications have variable or unpredictable workloads where the volume and complexity of database queries can change significantly. For example, a knowledge graph application for social media may see a sudden spike in queries due to sudden popularity.

Introducing Amazon Neptune Serverless
Today, we’re making that easier with the launch of Amazon Neptune Serverless. Neptune Serverless scales automatically as your queries and your workloads change, adjusting capacity in fine-grained increments to provide just the right amount of database resources that your application needs. In this way, you pay only for the capacity you use. You can use Neptune Serverless for development, test, and production workloads and optimize your database costs compared to provisioning for peak capacity.

With Neptune Serverless you can quickly and cost-effectively deploy graphs for your modern applications. You can start with a small graph, and as your workload grows, Neptune Serverless will automatically and seamlessly scale your graph databases to provide the performance you need. You no longer need to manage database capacity and you can now run graph applications without the risk of higher costs from over-provisioning or insufficient capacity from under-provisioning.

With Neptune Serverless, you can continue to use the same query languages (Apache TinkerPop Gremlin, openCypher, and RDF/SPARQL) and features (such as snapshots, streams, high availability, and database cloning) already available in Neptune.

Let’s see how this works in practice.

Creating an Amazon Neptune Serverless Database
In the Neptune console, I choose Databases in the navigation pane and then Create database. For Engine type, I select Serverless and enter my-database as the DB cluster identifier.

Console screenshot.

I can now configure the range of capacity, expressed in Neptune capacity units (NCUs), that Neptune Serverless can use based on my workload. I can now choose a template that will configure some of the next options for me. I choose the Production template that by default creates a read replica in a different Availability Zone. The Development and Testing template would optimize my costs by not having a read replica and giving access to DB instances that provide burstable capacity.

Console screenshot.

For Connectivity, I use my default VPC and its default security group.

Console screenshot.

Finally, I choose Create database. After a few minutes, the database is ready to use. In the list of databases, I choose the DB identifier to get the Writer and Reader endpoints that I am going to use later to access the database.

Using Amazon Neptune Serverless
There is no difference in the way you use Neptune Serverless compared to a provisioned Neptune database. I can use any of the query languages supported by Neptune. For this walkthrough, I choose to use openCypher, a declarative query language for property graphs originally developed by Neo4j that was open-sourced in 2015 and contributed to the openCypher project.

To connect to the database, I start an Amazon Linux Amazon Elastic Compute Cloud (Amazon EC2) instance in the same AWS Region and associate the default security group and a second security group that gives me SSH access.

With a property graph I can represent connected data. In this case, I want to create a simple graph that shows how some AWS services are part of a service category and implement common enterprise integration patterns.

I use curl to access the Writer openCypher HTTPS endpoint and create a few nodes that represent patterns, services, and service categories. The following commands are split into multiple lines in order to improve readability.

curl https://<my-writer-endpoint>:8182/openCypher \
-d "query=CREATE (mq:Pattern {name: 'Message Queue'}),
(pubSub:Pattern {name: 'Pub/Sub'}),
(eventBus:Pattern {name: 'Event Bus'}),
(workflow:Pattern {name: 'WorkFlow'}),
(applicationIntegration:ServiceCategory {name: 'Application Integration'}),
(sqs:Service {name: 'Amazon SQS'}), (sns:Service {name: 'Amazon SNS'}),
(eventBridge:Service {name: 'Amazon EventBridge'}), (stepFunctions:Service {name: 'AWS StepFunctions'}),
(sqs)-[:IMPLEMENT]->(mq), (sns)-[:IMPLEMENT]->(pubSub),
(eventBridge)-[:IMPLEMENT]->(eventBus),
(stepFunctions)-[:IMPLEMENT]->(workflow),
(applicationIntegration)-[:CONTAIN]->(sqs),
(applicationIntegration)-[:CONTAIN]->(sns),
(applicationIntegration)-[:CONTAIN]->(eventBridge),
(applicationIntegration)-[:CONTAIN]->(stepFunctions);"

This is a visual representation of the nodes and their relationships for the graph created by the previous command. The type (such as Service or Pattern) and properties (such as name) are shown inside each node. The arrows represent the relationships (such as CONTAIN or IMPLEMENT) between the nodes.

Visualization of graph data.

Now, I query the database to get some insights. To query the database, I can use either a Writer or a Reader endpoint. First, I want to know the name of the service implementing the “Message Queue” pattern. Note how the syntax of openCypher resembles that of SQL with MATCH instead of SELECT.

curl https://<my-endpoint>:8182/openCypher \
-d "query=MATCH (s:Service)-[:IMPLEMENT]->(p:Pattern {name: 'Message Queue'}) RETURN s.name;"
{
  "results" : [ {
    "s.name" : "Amazon SQS"
  } ]
}

I use the following query to see how many services are in the “Application Integration” category. This time, I use the WHERE clause to filter results.

curl https://<my-endpoint>:8182/openCypher \
-d "query=MATCH (c:ServiceCategory)-[:CONTAIN]->(s:Service) WHERE c.name='Application Integration' RETURN count(s);"
{
  "results" : [ {
    "count(s)" : 4
  } ]
}

There are many options now that I have this graph database up and running. I can add more data (services, categories, patterns) and more relationships between the nodes. I can focus on my application and let Neptune Serverless manage capacity and infrastructure for me.

Availability and Pricing
Amazon Neptune Serverless is available today in the following AWS Regions: US East (Ohio, N. Virginia), US West (N. California, Oregon), Asia Pacific (Tokyo), and Europe (Ireland, London).

With Neptune Serverless, you only pay for what you use. The database capacity is adjusted to provide the right amount of resources you need in terms of Neptune capacity units (NCUs). Each NCU is a combination of approximately 2 gibibytes (GiB) of memory with corresponding CPU and networking. The use of NCUs is billed per second. For more information, see the Neptune pricing page.

Having a serverless graph database opens many new possibilities. To learn more, see the Neptune Serverless documentation. Let us know what you build with this new capability!

Simplify the way you work with highly connected data using Neptune Serverless.

— Danilo

AWS Batch for Amazon Elastic Kubernetes Service

Post Syndicated from Steve Roberts original https://aws.amazon.com/blogs/aws/aws-batch-for-amazon-elastic-kubernetes-service/

Today I’m pleased to announce AWS Batch for Amazon Elastic Kubernetes Service (Amazon EKS). AWS Batch for Amazon EKS is ideal for customers who no longer want to shoulder the burden of configuring, fine-tuning, and managing Kubernetes clusters and pods to use with their batch processing workflows. Furthermore, there is no charge for this service. You only pay for the resources that your batch jobs launch.

When I’ve previously considered Kubernetes, it appeared to be focused on the management and hosting of microservice workloads. I was therefore surprised to discover that Kubernetes is also used by some customers to run large-scale, compute-intensive batch workloads. The differences between batch and microservice workloads mean that using Kubernetes for batch processing can be difficult and requires you to invest significant time in custom configuration and management to fine-tune a suitable solution.

Microservice and batch workloads on Kubernetes
Before we look further at AWS Batch for Amazon EKS, let’s consider some of the important differences between batch and microservice workloads to help set some context on why running batch workloads on Kubernetes can be difficult:

  • Microservice workloads are assumed to start and not stop—we expect them to be continuously available. In contrast, batch workloads run to completion and then exit—regardless of success or failure.
  • The results from a batch workload might not be available for several minutes—and sometimes hours or even days. Microservice workloads are expected to respond to requests within milliseconds.
  • We usually deploy microservice workloads across several Availability Zones to ensure high availability. This isn’t a requirement for batch workloads. Although we might distribute a batch job to allow it to process different input data in a distributed analysis, we more typically want to prioritize fast and optimal access to resources the job needs within the Availability Zone in which it is running.
  • Microservice and batch workloads scale differently. For microservices, scaling is generally predictable and usually linear as load increases (or decreases). With batch workloads, you might first perform an initial, or infrequently repeated, proof-of-concept run to analyze performance and discover the correct tuning needed for a full production run. The difference in size between the two can be exponential. Furthermore, with batch workloads, we might scale to an extreme level for a run, then scale back to zero instances for long periods of time, sometimes months.

Although third-party frameworks can help with running batch workloads on Kubernetes, you can also roll your own. Whichever approach you take, significant gaps and challenges can remain in handling the undifferentiated heavy lifting of building, configuring, and maintaining custom batch solutions. Then you also need to consider the scheduling, placing, and scaling of batch workloads on Kubernetes in a cost-effective manner. So how does AWS Batch on Amazon EKS help?

AWS Batch for Amazon EKS
AWS Batch for Amazon EKS offers a fully managed service to run batch workloads using clusters hosted on Amazon Elastic Compute Cloud (Amazon EC2) with no need to install and manage complex, custom batch solutions to address the differences highlighted earlier. AWS Batch provides a scheduler that controls and runs high-volume batch jobs, together with an orchestration component that evaluates when, where, and how to place jobs submitted to a queue. There’s no need for you, as the user, to coordinate any of this work—you just submit a job request into the queue.

Job queueing, dependency tracking, retries, prioritization, compute resource provisioning for Amazon Elastic Compute Cloud (EC2) and Amazon Elastic Compute Cloud (EC2) Spot, and pod submission are all handled using a serverless queue. As a managed service, AWS Batch for Amazon EKS enables you to reduce your operational and management overhead and focus instead on your business requirements. It provides integration with other services such as AWS Identity and Access Management (IAM), Amazon EventBridge, and AWS Step Functions and allows you to take advantage of other partners and tools in the Kubernetes ecosystem.

When running batch jobs on Amazon EKS clusters, AWS Batch is the main entry point to submit workload requests. Based on the queued jobs, AWS Batch then launches worker nodes in your cluster to process the jobs. These nodes are kept separate in a distinct namespace from your other node groups in Amazon EKS. Similarly, nodes in other pods are isolated from those used with AWS Batch.

How it works
AWS Batch uses managed Amazon EKS clusters, which need to be registered with AWS Batch, and permissions set so that AWS Batch can launch and manage compute environments in those clusters to process jobs submitted to the queue. You can find instructions on how to launch a managed cluster that AWS Batch can use in this topic in the Amazon EKS User Guide. Instructions for configuring permissions can be found in the AWS Batch User Guide.

Once one or more clusters have been registered, and permissions set, users can submit jobs to the queue. When a job is submitted, the following actions take place to process the request:

  • On receiving a job request, the queue dispatches a request to the configured compute environment for resources. If an AWS Batch managed scaling group does not yet exist, one is created, and AWS Batch then starts launching Amazon Elastic Compute Cloud (EC2) instances in the group. These new instances are added to the AWS Batch Kubernetes namespace of the cluster.
  • The Kubernetes scheduler places any configured DaemonSet on the node.
  • Once the node is ready, AWS Batch starts sending pod placement requests to your cluster, using labels and taints to make the placement choices for the pods, bypassing much of the logic of the k8s scheduler.
  • This process is repeated, scaling as needed across more EC2 instances in the scaling group until the maximum configured capacity is reached.
  • If the job queue has another compute environment defined, such as one configured to use Spot instances, it will launch additional nodes in that compute environment.
  • Once all work is complete, AWS Batch removes the nodes from the cluster, and terminates the instances.

These steps are illustrated in the animation below.

Animation showing the steps AWS Batch takes when processing a request using an Amazon EKS cluster

Start using your clusters with AWS Batch today
AWS Batch for Amazon Elastic Kubernetes Service (Amazon EKS) is available today. As I noted earlier, there is no charge for this service, and you pay only for the resources your jobs consume. To learn more, visit the Getting Started with Amazon EKS topic in the AWS Batch User Guide. There is also a self-guided workshop to help introduce you to AWS Batch on Amazon EKS.

— Steve

AWS Week in Review – October 24, 2022

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/aws-week-in-review-october-24-2022/

Last week, we announced plans to launch the AWS Asia Pacific (Bangkok) Region, which will become our third AWS Region in Southeast Asia. This Region will have three Availability Zones and will give AWS customers in Thailand the ability to run workloads and store data that must remain in-country.

In the Works – AWS Region in Thailand
With this big news, AWS announced a 190 billion baht (US 5 billion dollars) investment to drive Thailand’s digital future over the next 15 years. It includes capital expenditures on the construction of data centers, operational expenses related to ongoing utilities and facility costs, and the purchase of goods and services from Regional businesses.

Since we first opened an office in Bangkok in 2015, AWS has launched 10 Amazon CloudFront edge locations, a highly secure and programmable content delivery network (CDN) in Bangkok. In 2020, we launched AWS Outposts, a family of fully managed solutions delivering AWS infrastructure and services to virtually any on-premises or edge location for a truly consistent hybrid experience in Thailand. This year, we also plan the upcoming launch of an AWS Local Zone in Bangkok, which will enable customers to deliver applications that require single-digit millisecond latency to end users in Thailand.

Photo courtesy of Conor McNamara, Managing Director, ASEAN at AWS

The new AWS Region in Thailand is also part of our broader, multifaceted investment in the country, covering our local team, partners, skills, and the localization of services, including Amazon Transcribe, Amazon Translate, and Amazon Connect.

Many Thailand customers have chosen AWS to run their workloads to accelerate innovation, increase agility, and drive cost savings, such as 2C2P, CP All Plc., Digital Economy Promotion Agency, Energy Response Co. Ltd. (ENRES), PTT Global Public Company Limited (PTT), Siam Cement Group (SCG), Sukhothai Thammathirat Open University, The Stock Exchange of Thailand, Papyrus Studio, and more.

For example, Dr. Werner Vogels, CTO of Amazon.com, introduced the story of Papyrus Studio, a large film studio and one of the first customers in Thailand.

“Customer stories like Papyrus Studio inspire us at AWS. The cloud can allow a small company to rapidly scale and compete globally. It also provides new opportunities to create, innovate, and identify business opportunities that just aren’t possible with conventional infrastructure.”

For more information on how to enable AWS and get support in Thailand, contact our AWS Thailand team.

Last Week’s Launches
My favorite news of last week was to launch dark mode as a beta feature in the AWS Management Console. In Unified Settings, you can choose between three settings for visual mode: Browser default, Light, and Dark. Browser default applies the default dark or light setting of the browser, dark applies the new built-in dark mode, and light maintains the current look and feel of the AWS console. Choose your favorite!

Here are some launches that caught my eye for web, mobile, and IoT application developers:

New AWS Amplify Library for Swift – We announce the general availability of Amplify Library for Swift (previously Amplify iOS). Developers can use Amplify Library for Swift via the Swift Package Manager to build apps for iOS and macOS (currently in beta) platforms with Auth, Storage, Geo, and more features.

The Amplify Library for Swift is open source on GitHub, and we deeply appreciate the feedback we have gotten from the community. To learn more, see Introducing the AWS Amplify Library for Swift in the AWS Front-End Web & Mobile Blog or Amplify Library for Swift documentation.

New Amazon IVS Chat SDKs – Amazon Interactive Video Service (Amazon IVS) now provides SDKs for stream chat with support for web, Android, and iOS. The Amazon IVS stream chat SDKs support common functions for chat room resource management, sending and receiving messages, and managing chat room participants.

Amazon IVS is a managed, live-video streaming service using the broadcast SDKs or standard streaming software such as Open Broadcaster Software (OBS). The service provides cross-platform player SDKs for playback of Amazon IVS streams you need to make low-latency live video available to any viewer around the world. Also, it offers Chat Client Messaging SDK. For more information, see Getting Started with Amazon IVS Chat in the AWS documentation.

New AWS Parameters and Secrets Lambda Extension – This is new extension for AWS Lambda developers to retrieve parameters from AWS Systems Manager Parameter Store and secrets from AWS Secrets Manager. Lambda function developers can leverage this extension to improve their application performance as it decreases the latency and the cost of retrieving parameters and secrets.

Previously, you had to initialize either the core library of a service or the entire service SDK inside a Lambda function for retrieving secrets and parameters. Now you can simply use the extension. To learn more, see AWS Systems Manager Parameter Store documentation and AWS Secrets Manager documentation.

New FreeRTOS Long Term Support Version – We announce the second release of FreeRTOS Long Term Support (LTS) – FreeRTOS 202210.00 LTS. FreeRTOS LTS offers a more stable foundation than standard releases as manufacturers deploy and later update devices in the field. This release includes new and upgraded libraries such as AWS IoT Fleet Provisioning, Cellular LTE-M Interface, coreMQTT, and FreeRTOS-Plus-TCP.

All libraries included in this FreeRTOS LTS version will receive security and critical bug fixes until October 2024. With an LTS release, you can continue to maintain your existing FreeRTOS code base and avoid any potential disruptions resulting from FreeRTOS version upgrades. To learn more, see the FreeRTOS announcement.

Here is some news on performance improvement and increasing capacity:

Up to 10X Improving Amazon Aurora Snapshot Exporting Speed – Amazon Aurora MySQL-Compatible Edition for MySQL 5.7 and 8.0 now speed up to 10x faster snapshot exports to Amazon S3. The performance improvement is automatically applied to all types of database snapshot exports, including manual snapshots, automated system snapshots, and snapshots created by the AWS Backup service. For more information, see Exporting DB cluster snapshot data to Amazon S3 in the Amazon Aurora documentation.

3X Increasing Amazon RDS Read Capacity – Amazon Relational Database Service (RDS) for MySQL, MariaDB, and PostgreSQL now supports 15 read replicas per instance, including up to 5 cross-Region read replicas, delivering up to 3x the previous read capacity. For more information, see Working with read replicas in the Amazon RDS documentation.

2X Increasing AWS Snowball Edge Compute Capacity – The AWS Snowball Edge Compute Optimized device doubled the compute capacity up to 104 vCPUs, doubled the memory capacity up to 416GB RAM, and is now fully SSD with 28TB NVMe storage. The updated device is ideal when you need dense compute resources to run complex workloads such as machine learning inference or video analytics at the rugged, mobile edge such as trucks, aircraft or ships.  You can get started by ordering a Snowball Edge device on the AWS Snow Family console.

2X Increasing Amazon SQS FIFO Default Quota – Amazon Simple Queue Service (SQS) announces the increase of default quota up to 6,000 transactions per second per API action. It is double the previous 3,000 throughput quota for a high throughput mode for FIFO (first in, first out) queues in all AWS Regions where Amazon SQS FIFO queue is available. For a detailed breakdown of default throughput quotas per Region, see Quotas related to messages in the Amazon SQS documentation.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Here are some other news items that you may find interesting:

22 New or Updated Open Datasets on AWS – We released 22 new or updated datasets, including Amazonia-1 imagery, Bitcoin and Ethereum data, and elevation data over the Arctic and Antarctica. The full list of publicly available datasets is on the Registry of Open Data on AWS and is now also discoverable on AWS Data Exchange.

Sustainability with AWS Partners (ft. AWS On Air) – This episode covers a broad discipline of environmental, social, and governance (ESG) issues across all regions, organization types, and industries. AWS Sustainability & Climate Tech provides a comprehensive portfolio of AWS Partner solutions built on AWS that address climate change events and the United Nation’s Sustainable Development Goals (SDG).

AWS Open Source News and Updates #131 – This newsletter covers latest open-source projects such as Amazon EMR Toolkit for VS Code, a VS Code Extension to make it easier to develop Spark jobs on EMR and AWS CDK For Discourse, sample codes that demonstrates how to create a full environment for Discourse, etc. Remember to check out the Open source at AWS keep up to date with all our activity in open source by following us on @AWSOpen.

Upcoming AWS Events
Check your calendars and sign up for these AWS events:

AWS re:Invent 2022 Attendee Guide – Browse re:Invent 2022 attendee guides, curated by AWS Heroes, AWS industry teams, and AWS Partners. Each guide contains recommended sessions, tips and tricks for building your agenda, and other useful resources. Also, seat reservations for all sessions are now open for all re:Invent attendees. You can still register for AWS re:Invent either offline or online.

AWS AI/ML Innovation Day on October 25 – Join us for this year’s AWS AI/ML Innovation Day, where you’ll hear from Bratin Saha and other leaders in the field about the great strides AI/ML has made in the past and the promises awaiting us in the future.

AWS Container Day at Kubecon 2022 on October 25–28 – Come join us at KubeCon + CloudNativeCon North America 2022, where we’ll be hosting AWS Container Day Featuring Kubernetes on October 25 and educational sessions at our booth on October 26–28. Throughout the event, our sessions focus on security, cost optimization, GitOps/multi-cluster management, hybrid and edge compute, and more.

You can browse all upcoming in-person, and virtual events.

That’s all for this week. Check back next Monday for another Week in Review!

— Channy

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

AWS Week in Review – October 17, 2022

Post Syndicated from Steve Roberts original https://aws.amazon.com/blogs/aws/aws-week-in-review-october-17-2020/

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Monday means it’s time for another Week in Review post, so, without further ado, let’s dive right in!

Last Week’s Launches
Here’s some launch announcements from last week you may have missed.

AWS Directory Service for Microsoft Active Directory is now available on Windows Server 2019, and all new directories will run on this server platform. Those of you with existing directories can choose to update with either a few clicks on the AWS Managed Microsoft AD console, or you can update programmatically using an API. With either approach, you can update at a time convenient to you and your organization between now and March 2023. After March 2023, directories will be updated automatically.

Users of SAP Solution Manager can now use automated deployments to provision it, in accordance with AWS and SAP best practices, to both single-node and distributed architectures using AWS Launch Wizard.

AWS Activate is a program that offers free tools, resources, and the opportunity to apply for credits to smaller early stage businesses and also more advanced digital businesses, helping them get started quickly on AWS. The program is now open to any self-identified startup.

Amazon QuickSight users who employ row-level security (RLS) to control access to restricted datasets will be interested in a new feature that enables you to ask questions against topics in these datasets. User-based rules control the answers received to questions and any auto-complete suggestions provided when the questions are being framed. This ensures that users only ever receive answer data that they are granted permission to access.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
This interesting blog post focus on the startup Pieces Technologies, who are putting predictive artificial intelligence (AI) and machine learning (ML) tools to work on AWS to predict and offer clinical insights on patient outcomes such as such as projected discharge dates, anticipated clinical and non-clinical barriers to discharge, and risk of readmission. To help healthcare teams work more efficiently, the insights are provided in natural language and seek to optimize overall clarity of a patient’s clinical issues.

As usual, there’s another AWS open-source and updates newsletter. The newsletter is published weekly to bring you up to date on the latest news on open-source projects, posts, and events.

Upcoming Events
Speaking of upcoming events, the following are some you may be interested in joining, especially if you work with .NET:

Looking to modernize .NET workloads using Windows containers on AWS? There’s a free webinar, with follow-along lab, in just a couple of days on October 20. You can find more details and register here.

My .NET colleagues are also hosting another webinar on November 2 related to building modern .NET applications on AWS. If you’re curious about the hosting and development capabilities of AWS for .NET applications, this is a webinar you should definitely check out. You’ll find further information and registration here.

And finally, a reminder that reserved seating for sessions at AWS re:Invent 2022 is now open. We’re now just 6 weeks away from the event! There are lots of great sessions for your attention, but those of particular interest to me are the ones related to .NET, and at this year’s event we have seven breakouts, three chalk talks, and a workshop for you. You can find all the details using the .NET filter in the session catalog (the sessions all start with the prefix XNT, by the way).

That’s all for this week. Check back next Monday for another AWS Week in Review!

— Steve

Amazon EC2 Trn1 Instances for High-Performance Model Training are Now Available

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/amazon-ec2-trn1-instances-for-high-performance-model-training-are-now-available/

Deep learning (DL) models have been increasing in size and complexity over the last few years, pushing the time to train from days to weeks. Training large language models the size of GPT-3 can take months, leading to an exponential growth in training cost. To reduce model training times and enable machine learning (ML) practitioners to iterate fast, AWS has been innovating across chips, servers, and data center connectivity.

At AWS re:Invent 2021, we announced the preview of Amazon EC2 Trn1 instances powered by AWS Trainium chips. AWS Trainium is optimized for high-performance deep learning training and is the second-generation ML chip built by AWS, following AWS Inferentia.

Today, I’m excited to announce that Amazon EC2 Trn1 instances are now generally available! These instances are well-suited for large-scale distributed training of complex DL models across a broad set of applications, such as natural language processing, image recognition, and more.

Compared to Amazon EC2 P4d instances, Trn1 instances deliver 1.4x the teraFLOPS for BF16 data types, 2.5x more teraFLOPS for TF32 data types, 5x the teraFLOPS for FP32 data types, 4x inter-node network bandwidth, and up to 50 percent cost-to-train savings. Trn1 instances can be deployed in EC2 UltraClusters that serve as powerful supercomputers to rapidly train complex deep learning models. I’ll share more details on EC2 UltraClusters later in this blog post.

New Trn1 Instance Highlights
Trn1 instances are available today in two sizes and are powered by up to 16 AWS Trainium chips with 128 vCPUs. They provide high-performance networking and storage to support efficient data and model parallelism, popular strategies for distributed training.

Trn1 instances offer up to 512 GB of high-bandwidth memory, deliver up to 3.4 petaFLOPS of TF32/FP16/BF16 compute power, and feature an ultra-high-speed NeuronLink interconnect between chips. NeuronLink helps avoid communication bottlenecks when scaling workloads across multiple Trainium chips.

Trn1 instances are also the first EC2 instances to enable up to 800 Gbps of Elastic Fabric Adapter (EFA) network bandwidth for high-throughput network communication. This second generation EFA delivers lower latency and up to 2x more network bandwidth compared to the previous generation. Trn1 instances also come with up to 8 TB of local NVMe SSD storage for ultra-fast access to large datasets.

The following table lists the sizes and specs of Trn1 instances in detail.

Instance Name
 vCPUs AWS Trainium Chips Accelerator Memory NeuronLink Instance Memory Instance Networking Local Instance Storage
trn1.2xlarge 8 1 32 GB N/A 32 GB Up to 12.5 Gbps 1x 500 GB NVMe
trn1.32xlarge 128 16 512 GB Supported 512 GB 800 Gbps 4x 2 TB NVMe

Trn1 EC2 UltraClusters
For large-scale model training, Trn1 instances integrate with Amazon FSx for Lustre high-performance storage and are deployed in EC2 UltraClusters. EC2 UltraClusters are hyperscale clusters interconnected with a non-blocking petabit-scale network. This gives you on-demand access to a supercomputer to cut model training time for large and complex models from months to weeks or even days.

Amazon EC2 Trn1 UltraCluster

AWS Trainium Innovation
AWS Trainium chips include specific scalar, vector, and tensor engines that are purpose-built for deep learning algorithms. This ensures higher chip utilization as compared to other architectures, resulting in higher performance.

Here is a short summary of additional hardware innovations:

  • Data Types: AWS Trainium supports a wide range of data types, including FP32, TF32, BF16, FP16, and UINT8, so you can choose the most suitable data type for your workloads. It also supports a new, configurable FP8 (cFP8) data type, which is especially relevant for large models because it reduces the memory footprint and I/O requirements of the model.
  • Hardware-Optimized Stochastic Rounding: Stochastic rounding achieves close to FP32-level accuracy with faster BF16-level performance when you enable auto-casting from FP32 to BF16 data types. Stochastic rounding is a different way of rounding floating-point numbers, which is more suitable for machine learning workloads versus the commonly used Round Nearest Even rounding. By setting the environment variable NEURON_RT_STOCHASTIC_ROUNDING_EN=1 to use stochastic rounding, you can train a model up to 30 percent faster.
  • Custom Operators, Dynamic Tensor Shapes: AWS Trainium also supports custom operators written in C++ and dynamic tensor shapes. Dynamic tensor shapes are key for models with unknown input tensor sizes, such as models processing text.

AWS Trainium shares the same AWS Neuron SDK as AWS Inferentia, making it easy for everyone who is already using AWS Inferentia to get started with AWS Trainium.

For model training, the Neuron SDK consists of a compiler, framework extensions, a runtime library, and developer tools. The Neuron plugin natively integrates with popular ML frameworks, such as PyTorch and TensorFlow.

The AWS Neuron SDK supports just-in-time (JIT) compilation, in addition to ahead-of-time (AOT) compilation, to speed up model compilation, and Eager Debug Mode, for a step-by-step execution.

To compile and run your model on AWS Trainium, you need to change only a few lines of code in your training script. You don’t need to tweak your model or think about data type conversion.

Get Started with Trn1 Instances
In this example, I train a PyTorch model on an EC2 Trn1 instance using the available PyTorch Neuron packages. PyTorch Neuron is based on the PyTorch XLA software package and enables conversion of PyTorch operations to AWS Trainium instructions.

Each AWS Trainium chip includes two NeuronCore accelerators, which are the main neural network compute units. With only a few changes to your training code, you can train your PyTorch model on AWS Trainium NeuronCores.

SSH into the Trn1 instance and activate a Python virtual environment that includes the PyTorch Neuron packages. If you’re using a Neuron-provided AMI, you can activate the preinstalled environment by running the following command:

source aws_neuron_venv_pytorch_p36/bin/activate

Before you can run your training script, you need to make a few modifications. On Trn1 instances, the default XLA device should be mapped to a NeuronCore.

Let’s start by adding the PyTorch XLA imports to your training script:

import torch, torch_xla
import torch_xla.core.xla_model as xm

Then, place your model and tensors onto an XLA device:

model.to(xm.xla_device())
tensor.to(xm.xla_device())

When the model is moved to the XLA device (NeuronCore), subsequent operations on the model are recorded for later execution. This is XLA’s lazy execution which is different from PyTorch’s eager execution. Within the training loop, you have to mark the graph to be optimized and run on the XLA device using xm.mark_step(). Without this mark, XLA cannot determine where the graph ends.

...
for data, target in train_loader:
	output = model(data)
	loss = loss_fn(output, target)
	loss.backward()
	optimizer.step()
	xm.mark_step()
...

You can now run your training script using torchrun <my_training_script>.py.

When running the training script, you can configure the number of NeuronCores to use for training by using torchrun –nproc_per_node.

For example, to run a multi-worker data parallel model training on all 32 NeuronCores in one trn1.32xlarge instance, run torchrun --nproc_per_node=32 <my_training_script>.py.

Data parallel is a strategy for distributed training that allows you to replicate your script across multiple workers, with each worker processing a portion of the training dataset. The workers then share their result with each other.

For more details on supported ML frameworks, model types, and how to prepare your model training script for large-scale distributed training across trn1.32xlarge instances, have a look at the AWS Neuron SDK documentation.

Profiling Tools
Let’s have a quick look at useful tools to keep track of your ML experiments and profile Trn1 instance resource consumption. Neuron integrates with TensorBoard to track and visualize your model training metrics.

AWS Neuron SDK TensorBoard integration

On the Trn1 instance, you can use the neuron-ls command to describe the number of Neuron devices present in the system, along with the associated NeuronCore count, memory, connectivity/topology, PCI device information, and the Python process that currently has ownership of the NeuronCores:

AWS Neuron SDK neuron-ls command

Similarly, you can use the neuron-top command to see a high-level view of the Neuron environment. This shows the utilization of each of the NeuronCores, any models that are currently loaded onto one or more NeuronCores, process IDs for any processes that are using the Neuron runtime, and basic system statistics relating to vCPU and memory usage.

AWS Neuron SDK neuron-top command

Available Now
You can launch Trn1 instances today in the AWS US East (N. Virginia) and US West (Oregon) Regions as On-Demand, Reserved, and Spot Instances or as part of a Savings Plan. As usual with Amazon EC2, you pay only for what you use. For more information, see Amazon EC2 pricing.

Trn1 instances can be deployed using AWS Deep Learning AMIs, and container images are available via managed services such as Amazon SageMaker, Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Elastic Container Service (Amazon ECS), and AWS ParallelCluster.

To learn more, visit our Amazon EC2 Trn1 instances page, and please send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

— Antje

AWS Week in Review – October 10, 2022

Post Syndicated from Marcia Villalba original https://aws.amazon.com/blogs/aws/aws-week-in-review-october-10-2022/

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

I had an amazing start to the week last week as I was speaking at the AWS Community Day NL. This event had 500 attendees and over 70 speakers, and Dr. Werner Vogels, Amazon CTO, delivered the keynote. AWS Community Days are community-led conferences organized by local communities, with a variety of workshops and sessions. I recommend checking your region for any of these events.

Community Day NL

Last Week’s Launches
Here are some launches that got my attention during the previous week.

Amazon S3 Object Lambda now supports using your own code to change the results of HEAD and LIST requests, besides GET (which we launched last year). This feature now enables more capabilities for what you can do with S3 Object Lambda. Danilo made a Twitter thread with lots of use cases for this new launch.

Amazon SageMaker Clarify now can provide near real-time explanations for ML predictions. SageMaker Clarify is a service that provides explainability by ML models individual predictions. These explanations are important for developers to get visibility into their training data and models to identify potential bias.

AWS Storage Gateway now supports 15 TiB tapes. It increased the maximum supported virtual tape size on Tape Gateway from 5 TiB to 15 TiB, so you can store more data on a single virtual tape, and you can reduce the number of tapes you need to manage.

Amazon Aurora Serverless v2 now supports AWS CloudFormation. Early this year, we announced the general availability of Aurora Serverless v2, and now you can use AWS CloudFormation Templates to deploy and change the database along with the rest of your infrastructure.

AWS Config now supports 15 new resource types, including AWS DataSync, Amazon GuardDuty, Amazon Simple Email Service (Amazon SES), AWS AppSync, AWS Cloud Map, Amazon EC2, and AWS AppConfig. With this launch, you can use AWS Config to monitor configuration data for the supported resource types in your AWS account, and you can see how the configuration changes.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Some other updates and news that you may have missed:

This week an article about how AWS is leading a pilot project to turn the Greek island of Naxos into a smart island caught my attention. The project introduces smart solutions for mobility, primary healthcare, and the transport of goods. The solution has been built based on four pillars that were important for the island: sustainability, telehealth, leisure, and digital skills. Check out the whole article to learn what they are doing.

Podcast Charlas Técnicas de AWS – If you understand Spanish, this podcast is for you. Podcast Charlas Técnicas is one of the official AWS podcasts in Spanish, and every other week there is a new episode. The podcast is meant for builders, and it shares stories about how customers implemented and learned AWS services, how to architect applications, and how to use new services. You can listen to all the episodes directly from your favorite podcast app or at AWS Podcasts en español.

AWS open-source news and updates – This is a newsletter curated by my colleague Ricardo to bring you the latest open-source projects, posts, events, and more.

Upcoming AWS Events
Check your calendars and sign up for these AWS events:

AWS re:Invent reserved seating opens on October 11. If you are planning to attend, book a spot in advance for your favorite sessions. AWS re:Invent is our biggest conference of the year, it happens in Las Vegas from November 28 to December 2, and registrations are open. Many writers of this blog have sessions at re:Invent, and you can search the event agenda using our names.

I started the post talking about AWS Community Days, and there is one in Warsaw, Poland, on October 14. If you are around Warsaw during this week, you can first check out the AWS Pop-up Hub in Warsaw that runs October 10-14 and then join for the Community Day.

On October 20, there is a virtual event for modernizing .NET workloads with Windows containers on AWS, You can register for free.

That’s all for this week. Check back next Monday for another Week in Review!

— Marcia

AWS Local Zones Expansion: Taipei and Delhi

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-local-zones-expansion-taipei-and-delhi/

In late 2019 I told you about the AWS Local Zone in Los Angeles, California. In that post I described Local Zones as a new type of AWS infrastructure deployment that brings select AWS services very close to a particular geographic area. A year after that launch, I announced our plans to add 3 more Local Zones in 2020, and 12 more in 2021. Right now, we are working to bring Local Zones to 33 cities in 27 countries including 6 in Latin America.

Applications hosted in a Local Zone benefit from very low (single-digit millisecond) latency access to EC2 instances and other AWS services. Local Zones also give AWS customers additional choices regarding data residency, giving them the ability to store and process sensitive data (often financial or personal in nature) in-country.

Going Global
Today I am happy to announce the launch of Local Zones in Taipei (Taiwan) and Delhi (India). Like the existing Local Zones in the US, you start by enabling them in the AWS Management Console:

After you do this, you can launch Amazon Elastic Compute Cloud (Amazon EC2) instances, create Amazon Elastic Block Store (Amazon EBS) volumes,and make use of other services including Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Kubernetes Service (Amazon EKS), and Amazon Virtual Private Cloud (Amazon VPC). The new Local Zones include T3, C5, M5, R5, and G4dn instances in select sizes, along with General Purpose SSD (gp2) EBS volumes.

Local Zones in Action
AWS customers are working to put Local Zones to use. Here are a few use cases:

AdvaHealth Solutions – This digital health care & life sciences company supports radiology, oncology, ophthalmology and other medical imaging applications with AdvaPACS, a cloud-native image archive. The new Local Zones will allow them to deliver diagnostic image data with low latency in order to improve the point-of-care experience for patients and health care providers across Asia, and will also support expansion into new markets.

M2P Fintech specializes in building financial infrastructure and is an API infrastructure platform for Banking, Lending and Payments products. More than 600 Fintechs, 100 Banks & 100 Financial Institutions across MENA and APAC regions rely on M2P’s platform to power their own branded products including category leaders across ride-hailing, food delivery, and credit cards. M2P uses Local Zones instead of bearing the burden of setting up their own data centers and to meet local requirements for data processing and storage.

NaranjaX – This financial services company is the primary credit card issuer in Argentina. They are engaged in a digital transformation with the target of delivering an improved financial solution to their commercial customers, and believe that using Local Zones will give these customers a strategic advantage.

Pluto XR is the developer of the PlutoSphere OS that enables gamers, developers and operators to live stream XR applications to any XR device. In order to deliver a high quality streaming experience, they run their application as close to their end users as possible. The new Local Zones will allow them to serve millions of users in new metro areas

Riot Games is an American video game developer, publisher and entertainment company based in Los Angeles, California. Their games deliver an optimal player experience through ultra low latency for their MOBA (Multiplayer Online Battle Arena) League of Legends and their first-person tactical shooter VALORANT. By deploying their games into Local Zones, Riot is able to serve players at low latency without the need for operating on-premises compute.

Zenga Media is one of the largest media-tech companies in India. They provide live streaming and over-the-top distribution of entertainment content to millions of users globally, while using cloud-based video editing and sharing to process content destined for TV shows, sports broadcasts, news, and movies. They will use Local Zones to provide local connectivity to their editors and customers, thereby speeding processing and delivering a superior video streaming experience to customers.

Local Zones Resources
Here are a few resources to help you learn more about designing, building, and using Local Zones:

I am always interested in hearing about how our customers are making use of Local Zones. Leave me a comment or track me down online and let me know what you are working on!

— Jeff;

Announcing General Availability of Amazon Connect Cases

Post Syndicated from Veliswa Boya original https://aws.amazon.com/blogs/aws/announcing-general-availability-of-amazon-connect-cases/

In June 2022 AWS announced a preview of Amazon Connect Cases, a feature of Amazon Connect that simplifies these customer interactions and reduces the average handle times of issues.

Today I am excited to announce the general availability of Amazon Connect Cases. Cases, a feature of Amazon Connect, makes it easy for your contact center agents to create, collaborate on, and quickly resolve customer issues that require several customer conversations and follow-up tasks, and they can focus on solving the customer issue, no matter how simple or how complex. Agents have relevant case details (such as date and time opened, issue summary, or customer information) in a single unified view, and they can focus on solving the customer issue.

Getting started with Cases takes only a few clicks because it is built into Amazon Connect. With Cases, you automatically create cases or find existing cases, saving agents time searching and entering data manually. Cases accelerates resolution times, improves efficiency, and reduces errors to help increase customer satisfaction.

Best of all, Cases is part of the unified agent application that also includes the Amazon Connect Contact Control Panel to handle contacts, Amazon Connect Customer Profiles to identify the customer and personalize the experience, Amazon Connect Wisdom to surface relevant knowledge articles, and Amazon Connect Tasks to automate, track, and monitor follow up items.

An Overview of Amazon Connect Cases

Litigation Practice Group is a provider of legal support for debt relief. Litigation’s Director of Business Intelligence, Alex Miles, spoke about how they have experienced Cases. He said:

“Amazon Connect not only addresses many of the technological limitations we were facing but brings with it a suite of modern solutions for all our business needs. One of those needs is case management to handle operating activities, including payments, document control, and legal cases. Amazon Connect Cases seamlessly integrates with our existing contact center workflows. Our agents and legal teams now have full performance visibility and spend less time on manual tasks, creating more time to find solutions to enhance the customer journey.”

Cases provides built-in case management capabilities, eliminating the need for contact centers to build custom solutions or integrate with third-party products to handle complex customer
issues. For every issue, Cases enables agents to view case history and activity all in one place, automatically capture case data from interactive voice response (IVR) or chats (via Amazon Lex), and track follow-up work with Tasks.

  1. View case history and activity all in one place – Agents view the details of the customer issue (including calls, tasks, and chats associated with the case) all in one place within the unified Amazon Connect agent application. The timeline view shows agents a case at a glance, removing the need for agents to go back and forth between applications.

    View case history and activity in one place

    View case history and activity in one place

  2. Automatically capture case data from interactive voice response (IVR) or chats – With this feature you can automatically create and update cases by using information gathered in a customer’s self-service IVR or chatbot interaction. When agent assistance is required, the contact will then be routed to an available agent with the relevant case attached, resulting in improved average handle time and first-contact resolution.

    Automatically capture case data from your IVR and chatbots

    Automatically capture case data from your IVR and chatbots

  3. Take action with task management – This feature is Cases working together with Amazon Connect Tasks to help you reduce resolution time and improve efficiency. Tasks, which tracks the work that must be done to resolve the customer’s issue, ensures that a case is captured and includes prior and pending actions needed to resolve the issue. This makes it easier for agents to create, prioritize, and monitor work assigned to other agents or teams. Here I’d also like to highlight how all this results in great collaboration between agents and ultimately, teams.

    Take action with task management

    Take action with task management

  4. Get started in a few clicks! Turn on Cases and configure permissions, fields, and templates, all within Amazon Connect. No third-party tools or integrations are required.
    Get Started

    Get Started

General Availability
Amazon Connect Cases is generally available in US East (N. Virginia), and US West (Oregon).

– Veliswa x

AWS Week in Review – October 3, 2022

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/aws-week-in-review-october-3-2022/

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

A new week and a new month just started. Curious which were the most significant AWS news from the previous seven days? I got you covered with this post.

Last Week’s Launches
Here are the launches that got my attention last week:

Amazon File Cache – A high performance cache on AWS that accelerates and simplifies demanding cloud bursting and hybrid workflows by giving access to files using a fast and familiar POSIX interface, no matter if the original files live on premises on any file system that can be accessed through NFS v3 or on S3.

Amazon Data Lifecycle Manager – You can now automatically archive Amazon EBS snapshots to save up to 75 percent on storage costs for those EBS snapshots that you intend to retain for more than 90 days and rarely access.

AWS App Runner – You can now build and run web applications and APIs from source code using the new Node.js 16 managed runtime.

AWS Copilot – The CLI for containerized apps adds IAM permission boundaries, support for FIFO SNS/SQS for the Copilot worker-service pattern, and using Amazon CloudFront for low-latency content delivery and fast TLS-termination for public load-balanced web services.

Bottlerocket – The Linux-based operating system purpose-built to run container workloads is now supported by Amazon Inspector. Amazon Inspector can now recommend an update of Bottlerocket if it finds a vulnerability.

Amazon SageMaker Canvas – Now supports mathematical functions and operators for richer data exploration and to understand the relationships between variables in your data.

AWS Compute Optimizer – Now provides cost and performance optimization recommendations for 37 new EC2 instance types, including bare metal instances (m6g.metal) and compute optimized instances (c7g.2xlarge, hpc6a.48xlarge), and new memory metrics for Windows instances.

AWS Budgets – Use a simplified 1-click workflow for common budgeting scenarios with step-by-step tutorials on how to use each template.

Amazon Connect – Now provides an updated flow designer UI that makes it easier and faster to build personalized and automated end-customer experiences, as well as a queue dashboard to view and compare real-time queue performance through time series graphs.

Amazon WorkSpaces – You can now provision Ubuntu desktops and use virtual desktops for new categories of workloads, such as for your developers, engineers, and data scientists.

Amazon WorkSpaces Core – A fully managed infrastructure-only solution for third-party Virtual Desktop Infrastructure (VDI) management software that simplifies VDI migration and combines your current VDI software with the security and reliability of AWS. Read more about it in this Desktop and Application Streaming blog post.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
A few more blog posts you might have missed:

Introducing new language extensions in AWS CloudFormation – In this Cloud Operations & Migrations blog post, we introduce the new language transform that enhances CloudFormation core language with intrinsic functions that simplify handling JSON strings (Fn::ToJsonString), array lengths (Fn::Length), and update and deletion policies.

Building a GraphQL API with Java and AWS Lambda – This blog shows different options for resolving GraphQL queries using serverless technologies on AWS.

For AWS open-source news and updates, here’s the latest newsletter curated by Ricardo to bring you the most recent updates on open-source projects, posts, events, and more.

Upcoming AWS Events
As usual, there are many opportunities to meet:

AWS Summits– Connect, collaborate, and learn about AWS at these free in-person events: Bogotá (October 4), and Singapore (October 6).

AWS Community Days – AWS Community Day events are community-led conferences to share and learn together. Join us in Amersfoort, Netherlands (on October 3, today), Warsaw, Poland (October 14), and Dresden, Germany (October 19).

That’s all from me for this week. Come back next Monday for another Week in Review!

— Danilo

New Hands-On Course for Business Analysts – Practical Decision Making using No-Code ML on AWS

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/new-hands-on-course-for-business-analysts-practical-decision-making-using-no-code-ml-on-aws/

Artificial intelligence (AI) is all around us. AI sends certain emails to our spam folders. It powers autocorrect, which helps us fix typos when we text. And now we can use it to solve business problems.

In business, data-driven insights have become increasingly valuable. These insights are often discovered with the help of machine learning (ML), a subset of AI and the foundation of complex AI systems. And ML technology has come a long way. Today, you don’t need to be a data scientist or computer engineer to gain insights. With the help of no-code ML tools such as Amazon SageMaker Canvas, you can now achieve effective business outcomes using ML without writing a single line of code. You can better understand patterns, trends, and what’s likely to happen in the future. And that means making better business decisions!

Today, I’m happy to announce that AWS and Coursera are launching the new hands-on course Practical Decision Making using No-Code ML on AWS. This five-hour course is designed to demystify AI/ML and give anyone with a spreadsheet the ability to solve real-life business problems.

Practical Decision Making on Coursera

Course Highlights
Over the course of three lessons, you will learn how to address your business problem using ML, how to build and understand an ML model without any code, and how to use ML to extract value to make better decisions. Each lesson walks you through real-life business scenarios and hands-on exercises using Amazon SageMaker Canvas, a visual, no-code ML tool.

Lesson 1 – How To Address Your Business Problem Using ML
In the first lesson, you will learn how to address your business problem using ML without knowing data science. You will be able to describe the four stages of analytics and discuss the high-level concepts of AI/ML.

Practical Data Science - Prescriptive Analytics

This lesson will also introduce you to automated machine learning (AutoML) and how AutoML can help you generate insights based on common business use cases. You will then practice forming business questions around the most common machine learning problem types.

Practical Decision Making - Forming ML questions

For example, imagine you are a business analyst at a ticketing company. You manage ticket sales for large venues—concerts, sporting events, and so on. Let’s assume you want to predict cash flow. A question to solve with ML could be: “How can you better forecast ticket sales?” This is an example of time series forecasting. You will also explore numeric and category ML problems throughout the course. They will help you answer business questions such as “What’s the likely annual revenue for a customer?” and “Will this customer buy another ticket in the next three months?”.

Next, you will learn about the iterative process of asking questions for machine learning to make the questions more explicit and explore how to pick the highest value problems to work on.

Practical Decision Making - Value vs. Ease

The first lesson wraps up with a deep dive on how time influences your data across forecasting and nonforecasting business problems and how to set up your data for each ML problem type.

Lesson 2 – Build and Understand an ML Model Without Any Code
In the second lesson, you learn how to build and understand an ML model without any code using Amazon SageMaker Canvas. You will focus on a customer churn example with synthetically generated data from a cellular services company. The problem question is, “Which customers are most likely to cancel their service next month?”

Practical Decision Making - Customer Churn Example

You will learn how to import data and start exploring it. This lesson will explain how to select the right configuration, pick the target column, and show you how to prepare your data for ML.

SageMaker Canvas also recently introduced new visualizations for exploratory data analysis (EDA), including scatter plots, bar charts, and box plots. These visualizations help you analyze the relationships between features in your data sets and comprehend your data better.

Practical Decision Making - SageMaker Canvas Scatter Plot

After a final data validation, you can preview the model. This shows you right away how accurate the model might be and, on average, which features or columns have the greatest relative impact on model predictions. Once you are done preparing and validating the data, you can go ahead and build the model.

Practical Decision Making - Model Evaluation

Next, you will learn how to evaluate the performance of the model. You will be able to describe the difference between training data and test data splits and how they are used to derive the model’s accuracy score. The lesson also discusses additional performance metrics and how you can apply domain knowledge to decide if the model is performing well. Once you understand how to evaluate the performance metrics, you have the foundation for making better business decisions.

The second lesson wraps up with some common gotchas to watch out for and shows how to iterate on the model to keep improving performance. You will be able to describe the concept of data leakage as a result of memorization versus generalization and additional model flaws to avoid. You will also learn how to iterate on questions, included features, and sample sizes to keep increasing model performance.

Lesson 3 – Extract Value From ML
In the third lesson, you learn how to extract value from ML to make better decisions. You will be able to generate and read predictions, including predictions on a single row of a spreadsheet, called a single prediction, and predictions on the entire spreadsheet, called batch prediction. You will be able to understand what is impacting predictions and play with different scenarios.

Next, you will learn how to share insights and predictions with others. You will learn how to take visuals from the product, such as feature importance charts or scoring diagrams, and share the insights through presentations or business reports.

The third lesson wraps up with how to collaborate with the data science team or a team member with machine learning expertise. When you build your model using SageMaker Canvas, you can choose either a Quick build or a Standard build. The Quick build usually takes 2–15 minutes and limits the input dataset to a maximum of 50,000 rows. The Standard build usually takes 2–4 hours and generally has a higher accuracy. SageMaker Canvas makes it easy to share a standard build model. In the process, you can reveal the model’s behind-the-scenes complexity down to the code level.

Once you have the trained model open, you can click on the Share button. This creates a link that can be opened in SageMaker Studio, an integrated development environment used by data science teams.

Practical Decision Making - Share Model

In SageMaker Studio, you can see the transformations to the input data set and detailed information about scoring and artifacts, like the model object. You can also see the Python notebooks for data exploration and feature engineering.

Practical Decision Making - SageMaker Studio

Hands-On Exercises
This course includes seven hands-on labs to put your learning into practice. You will have the opportunity to use no-code ML with SageMaker Canvas to solve real-world challenges based on publicly available datasets.

The labs focus on different business problems across industries, including retail, financial services, manufacturing, healthcare, and life sciences, as well as transport and logistics.

You will have the opportunity to work on customer churn predictions, housing price predictions, sales forecasting, loan predictions, diabetic patient readmission prediction, machine failure predictions, and supply chain delivery on-time predictions.

Register Today
Practical Decision Making using No-Code ML on AWS is a five-hour course for business analysts and anyone who wants to learn how to solve real-life business problems using no-code ML.

Sign up for Practical Decision Making using No-Code ML on AWS today at Coursera!

— Antje

AWS IoT FleetWise Now Generally Available – Easily Collect Vehicle Data and Send to the Cloud

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/aws-iot-fleetwise-now-generally-available-easily-collect-vehicle-data-and-send-to-the-cloud/

Today we announce the general availability of AWS IoT FleetWise, a fully managed AWS service that makes it easier to collect, transform, and transfer vehicle data to the cloud. Last AWS re:Invent 2021, we previewed AWS IoT FleetWise, heard customer feedback, and improved features for various use cases of near-real-time vehicle data processing.

With AWS IoT FleetWise, automakers, fleet operators, and automotive suppliers can take the complex variability out of collecting data from vehicle fleets at scale. You can access standardized fleet-wide vehicle data and avoid developing custom data collection systems, or you can integrate AWS IoT FleetWise to enhance your existing systems. AWS IoT FleetWise enables intelligent data collection that sends the exact data you need from the vehicle to the cloud. You can use the data to analyze vehicle fleet health to more quickly identify potential maintenance issues or make in-vehicle infotainment systems smarter. Furthermore, you can use it to train machine learning (ML) models that improve autonomous driving and advanced driver assistance systems (ADAS).

For example, electric vehicle (EV) battery temperature is a critical metric that should be continuously analyzed for the entire vehicle fleet. In order to avoid costly continuous data ingestion, you may want to optimize the data collection by setting a threshold on EV battery temperature. The results of this analysis would be provided to the automaker’s quality engineering department, enabling fast assessment of the criticality and possible root causes of any issues identified at certain temperatures. Based on the root cause analysis, the automaker can then take short-term actions to support the driver affected by the issue, as well as midterm actions to improve vehicle quality.

How AWS IoT FleetWise Works
AWS IoT FleetWise provides a vehicle modeling framework that you can use to model your vehicle and its sensors and actuators in the cloud. To enable secure communication between your vehicle and the cloud, AWS IoT FleetWise also provides the AWS IoT FleetWise Edge Agent application that you can use to download and install in-vehicle electronic control units (ECUs) such as the gateway, in-vehicle infotainment controller, etc. You define data collection schemes in the cloud and deploy them to your vehicle.

The AWS IoT FleetWise Edge Agent running in your vehicle uses data collection schemes to control what data to collect and when to transfer it to the cloud. Data collected and ingested through AWS IoT FleetWise Edge Agent software goes directly into your Amazon Timestream table or Amazon Simple Storage Service (Amazon S3) repositories via AWS IoT Core.

AWS IoT FleetWise Features
To get started with AWS IoT FleetWise, you can register your account and configure the settings via the AWS console. AWS IoT FleetWise automatically registers your AWS account, IAM role, and Amazon Timestream resources.

The Edge Agent software is a C++ application distributed as source code and is available on GitHub to collect, decode, normalize, cache, and ingest vehicle data to AWS. It supports multiple deployment options, such as vehicle gateways, infotainment systems, telematics control units (TCUs), or aftermarket devices. When vehicles are connected to the cloud, the Edge Agent continually receives data collection schemes and collects, decodes, normalizes and ingests the transformed vehicle data to AWS.

Let’s see the benefits and features of AWS IoT FleetWise:

Signal catalog
A signal catalog contains a collection of vehicle signals. Signals are fundamental structures that you define to contain vehicle data and its metadata. A signal can be a sensor and its status, an attribute as static information of the manufacturer, a branch to represent a nested structure such as Vehicle.Powertrain.combustionEngine expression, or an actuator such as the state of a vehicle device. For example, you can create a sensor to receive in-vehicle temperature values and store its metadata, including a sensor name, a data type, and a unit.

Signals in a signal catalog can be used to model vehicles that use different protocols and data formats. For example, there are two cars made by different automakers: one uses the Controller Area Network (CAN) to transmit the in-vehicle temperature data and the other uses On-board Diagnostic (OBD) protocol.

You can define a sensor in the signal catalog to receive in-vehicle temperature values. This sensor can be used to represent the thermocouples in both cars, irrespective of how this temperature data is available within the vehicle networks. For more information, see Create and manage signal catalogs in the AWS documentation.

Vehicle models
Vehicle models are virtual declarative representations that standardize the format of your vehicles and define relationships between signals in the vehicles. Vehicle models enforce consistent information across multiple vehicles of the same type so that you can quickly configure and create a vehicle fleet. In each vehicle model, you can add signals, including attributes, branches (signal hierarchies), sensors, and actuators.

You can define condition-based schemes to control what data to collect, such as data in-vehicle temperature values that are greater than 40 degrees. You can also define time-based schemes to control how often to collect data. For more information, see Create and manage vehicle models in the AWS documentation.

When a decoder manifest is associated with a vehicle model, you can create a vehicle. Each vehicle corresponds to an AWS IoT thing. You can use an existing AWS IoT thing to create a vehicle or set AWS IoT FleetWise to automatically create an AWS IoT thing for your vehicle. For more information, see Provision vehicles in the AWS documentation. After you create vehicles, you can create campaigns for them.

Campaigns
A campaign gives the AWS IoT FleetWise Edge Agent instructions on how to select, collect, and transfer data to the cloud. You can make a campaign with vehicle attributes that you added when creating vehicles, and a data collection scheme. You can manually define the data collection scheme either condition-based logical expressions such as $variable.myVehicle.InVehicleTemperature > 40.0, or time-based data collection in milliseconds such as from 10000 – 60000 milliseconds. To learn more, see Create a campaign in the AWS documentation.

After you create and approve the campaign, AWS IoT FleetWise automatically deploys the campaign to the listed vehicles. The AWS IoT FleetWise Edge Agent software doesn’t start collecting data until a running campaign is deployed to the vehicle. If you want to pause collecting data from vehicles connected to the campaign, on the Campaign summary page, choose Suspend. To resume collecting data from vehicles connected to the campaign, choose Resume.

Demo – Visualizing Vehicle Data
Here is a demo that aims to show how AWS IoT FleetWise can make it easy to collect vehicle data and use it to build visualizing applications. In this demo, you can simulate two kinds of vehicles, an NXP GoldBox powered by an Automotive Grade Linux distribution that runs the AWS IoT FleetWise agent as an AWS IoT Greengrass component or a completely virtual vehicle implemented as an AWS Graviton ARM-based Amazon EC2 instance. To learn more, see the getting started guide and source code in the GitHub repository.

The vehicle in CARLA Simulator can self-drive or be driven with a game steering wheel connected to your desktop. You can watch a live demo video.

Data is collected by AWS IoT FleetWise and stored in the Amazon Timestream table, and visualized on a Grafana Dashboard.

Customer and Partner Voices
During the preview period, we heard lots of feedback from our customers and partners in automotive industry such as automakers, fleet operators, and automotive suppliers.

For example, Hyundai Motor Group (HMG) is a global vehicle manufacturer that offers consumers a technology-rich lineup of cars, sport utility vehicles, and electrified vehicles. HMG has used AWS services, such as using Amazon SageMaker, to reduce its ML model training time for autonomous driving models.

Hae Young Kwon, vice president and head of the infotainment development group at HMG, said:

“As a leading global vehicle manufacturer, we have come to appreciate the breadth and depth of AWS services to help create new connected vehicle capabilities. With more data available from our expanding global fleet of connected cars, we look forward to leveraging AWS IoT FleetWise to discover how we can build more personalized ownership experiences for our customers.”

LG CNS is a global IT service provider and AWS Premier Consulting Partner that is transforming smart transportation services by building an advanced transportation system that is convenient and safe by maximizing the operational efficiency of multiple modes of transport, including buses, subways, taxis, railways, and airplanes.

Jae Seung Lee, vice president at LG CNS, said:

“At LG CNS, we are committed to advancing the technology that is powering the future of transportation. By using AWS IoT FleetWise, we are creating a new data platform that allows us to ingest, analyze, and simulate vehicle conditions in real-time. With these advanced insights, our customers can gain a better understanding of their vehicles and, as a result, improve decision-making about their fleets.”

Bridgestone is a global leader in tires and rubber building on its expertise to provide solutions for safe and sustainable mobility. Bridgestone has worked with AWS for several years to develop a system that delivers insights derived from the interaction between a tire and a vehicle using advanced machine learning capabilities on Amazon SageMaker.

Brian Goldstine, president of mobility solutions and fleet management at Bridgestone Americas Inc. said:

“Bridgestone has been working with AWS to transform the digital services we provide to our automotive manufacturer, fleet, and retail customers. We look forward to exploring how AWS IoT FleetWise will make it easier for our customers to collect detailed tire data, which can provide new insights for their products and applications.”

Renesas Electronics Corporation is a global leader in microcontrollers, analog, power, and system on chips (SoC) products. Renesas launched cellular-to-cloud IoT development platforms and its cloud development kits to run on AWS IoT Core and FreeRTOS.

Yusuke Kawasaki, director at Renesas Electronics Corporation, said:

“The volume of connected vehicle data is forecast to increase dramatically over the next few years, driven by new and evolving customer expectations. As a result, Renesas is focused on addressing the needs of automotive engineers facing increasing system complexity. Incorporating AWS IoT FleetWise into our vehicle gateway solution will enable our customers to enjoy our market-ready approach for large-scale data collection and accelerate their cloud development strategy. We look forward to further collaborating with AWS to provide a better and simpler development environment for our customers.”

By working with AWS IoT FleetWise Partners, you can take advantage of solutions to streamline your IoT projects, reduce the risk of your efforts, and accelerate time to value. To learn more how AWS accelerates the automotive industry’s digital transformation, see AWS for Automotive.

Now Available
AWS IoT FleetWise is now generally available in the US East (N. Virginia) and Europe (Frankfurt) Regions. You pay for the vehicles you have created and messages per vehicle per month. Additional services used alongside AWS IoT FleetWise, such as AWS IoT Core and Amazon Timestream, are billed separately. For more detail, see the AWS IoT FleetWise pricing page.

To learn more, see the AWS IoT FleetWise resources page including documentations, videos, and blog posts. Please send feedback to AWS re:Post for AWS IoT FleetWise or through your usual AWS support contacts.

– Channy

AWS Week In Review — September 26, 2022

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/aws-week-in-review-september-26-2022/

It looks like my travel schedule is coupled with this Week In Review series of blog posts. This week, I am traveling to Fort-de-France in the French Caribbean islands to meet our customers and partners. I enjoy the travel time when I am offline. It gives me the opportunity to reflect on the past or plan for the future.

Last Week’s Launches
Here are some of the launches that caught my eye last week:

Amazon SageMaker Autopilot – has added a new Ensemble training mode powered by AutoGluon that is 8X faster than the current Hyper parameter Optimization Mode and supports a wide range of algorithms, including LightGBM, CatBoost, XGBoost, Random Forest, Extra Trees, linear models, and neural networks based on PyTorch and FastAI.

AWS Outposts and Amazon EKS – You can now deploy both the worker nodes and the Kubernetes control plane on an Outposts rack. This allows you to maximize your application availability in case of temporary network disconnection on premises. The Kubernetes control plane continues to manage the worker nodes, and no pod eviction happens when on-premises network connectivity is reestablished.

Amazon Corretto 19 – Corretto is a no-cost, multiplatform, production-ready distribution of OpenJDK. Corretto is distributed by Amazon under an open source license. This version supports the latest OpenJDK feature release and is available on Linux, Windows, and macOS. You can download Corretto 19 from our downloads page.

Amazon CloudWatch Evidently – Evidently is a fully-managed service that makes it easier to introduce experiments and launches in your application code. Evidently adds support for Client Side Evaluations (CSE) for AWS Lambda, powered by AWS AppConfig. Evidently CSE allows application developers to generate feature evaluations in single-digit milliseconds from within their own Lambda functions. Check the client-side evaluation documentation to learn more.

Amazon S3 on AWS Outposts – S3 on Outposts now supports object versioning. Versioning helps you to locally preserve, retrieve, and restore each version of every object stored in your buckets. Versioning objects makes it easier to recover from both unintended user actions and application failures.

Amazon Polly – Amazon Polly is a service that turns text into lifelike speech. This week, we announced the general availability of Hiujin, Amazon Polly’s first Cantonese-speaking neural text-to-speech (NTTS) voice. With this launch, the Amazon Polly portfolio now includes 96 voices across 34 languages and language variants.

X in Y – We launched existing AWS services in additional Regions:

Other AWS News
Introducing the Smart City Competency program – The AWS Smart City Competency provides best-in-class partner recommendations to our customers and the broader market. With the AWS Smart City Competency, you can quickly and confidently identify AWS Partners to help you address Smart City focused challenges.

An update to IAM role trust policy behavior – This is potentially a breaking change. AWS Identity and Access Management (IAM) is changing an aspect of how role trust policy evaluation behaves when a role assumes itself. Previously, roles implicitly trusted themselves. AWS is changing role assumption behavior to always require self-referential role trust policy grants. This change improves consistency and visibility with regard to role behavior and privileges. This blog post shares the details and explains how to evaluate if your roles are impacted by this change and what to modify. According to our data, only 0.0001 percent of roles are impacted. We notified by email the account owners.

Amazon Music Unifies Music Queuing – The Amazon Music team published a blog post to explain how they created a unified music queue across devices. They used AWS AppSync and AWS Amplify to build a robust solution that scales to millions of music lovers.

Upcoming AWS Events
Check your calendar and sign up for an AWS event in your Region and language:

AWS re:Invent – Learn the latest from AWS and get energized by the community present in Las Vegas, Nevada. Registrations are open for re:Invent 2022 which will be held from Monday, November 28 to Friday, December 2.

AWS Summits – Come together to connect, collaborate, and learn about AWS. Registration is open for the following in-person AWS Summits: Bogotá (October 4), and Singapore (October 6).

Natural Language Processing (NLP) Summit – The AWS NLP Summit 2022 will host over 25 sessions focusing on the latest trends, hottest research, and innovative applications leveraging NLP capabilities on AWS. It is happening at our UK headquarters in London, October 5–6, and you can register now.

AWS Innovate for every app – This regional online conference is designed to inspire and educate executives and IT professionals about AWS. It offers dozens of technical sessions in eight languages (English, Spanish, French, German, Italian, Japanese, Korean, and Indonesian). Register today: Americas, September 28; Europe, Middle-East, and Africa, October 6; Asia Pacific & Japan, October 20.

AWS Innovate for every app

AWS Community Days – AWS Community Day events are community-led conferences to share and learn with one another. In September, the AWS community in the US will run events in Arlington, Virginia (September 30). In Europe, Community Day events will be held in October. Join us in Amersfoort, Netherlands (October 3), Warsaw, Poland (October 14), and Dresden, Germany (October 19).

AWS Tour du Cloud – The AWS Team in France has prepared a roadshow to meet customers and partners with a one-day free conference in seven cities across the country (Aix en Provence, Lille, Toulouse, Bordeaux, Strasbourg, Nantes, and Lyon), and in Fort-de-France, Martinique. Tour du Cloud France

AWS Fest – This third-party event will feature AWS influencers, community heroes, industry leaders, and AWS customers, all sharing AWS optimization secrets (this week on Wednesday, September). You can register for AWS Fest here.

Stay Informed
That is my selection for this week! To better keep up with all of this news, please check out the following resources:

— seb
This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

AWS Week in Review – September 19, 2022

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-week-in-review-september-19-2022/

Things are heating up in Seattle, with preparation for AWS re:Invent 2022 well underway. Later this month the entire News Blog team will participate in our now-legendary “speed storming” event. Over the course of three or four days, each of the AWS service teams with a launch in the works for re:Invent will give us an overview and share their PRFAQ (Press Release + FAQ) with us. After the meetings conclude, we’ll divvy up the launches and get to work on our blog posts!

Last Week’s Launches
Here are some of the launches that caught my eye last week:

Amazon Lex Visual Conversation Builder – This new tool makes bot design easier than ever. You get a complete view of the conversation in one place, and you can manage complex conversations that have dynamic paths. To learn more and see the builder in action, read Announcing Visual Conversation Builder for Amazon Lex on the AWS Machine Learning Blog.

AWS Config Conformance Pack Price Reduction – We have reduced the price for evaluation of AWS Config Conformance Packs by up to 58%. These packs contain AWS Config rules and remediation actions that can be deployed as a single entity in account and a region, or across an entire organization. The price reduction took effect September 14, 2022; it lowers the cost per evaluation and decreases the number of evaluations needed to reach each pricing tier.

CDK (Cloud Development Kit) Tree View – The AWS CloudFormation console now includes a Constructs tree view that automatically organizes the resources that were synthesized by AWS CDK constructs. The top level of the tree view includes the named constructs and the second level includes all of the resources generated by the named construct. Read the What’s New to learn more!

AWS Incident Detection and Response – AWS Enterprise Support customers now have access to proactive monitoring and incident management for selected workloads running on AWS. As part of the onboarding process, AWS experts review workloads for reliability and operational excellence, and work with the customer to identify critical metrics and associated alarms. Incident Management Engineers then monitor the workloads, detect critical incidents, and initiate a call bridge to accelerate recovery. Read the AWS Incident Detection and Response page and the What’s New to learn more.

ECS Cluster Scale-In Speed – Auto-Scaled ECS clusters can now scale-in (reduce capacity) faster than ever before. Previously, each scale-in would reduce the capacity within an Auto Scaling Group (ASG) by 5% at a time. Now, capacity can be reduced by up to 50%. This change makes scaling more responsive to workload changes while still maintaining availability for spiky traffic patterns. Read Faster Scaling-In for Amazon ECS Cluster Auto Scaling and the What’s New to learn more.

AWS Outposts Rack Networking – AWS Outposts racks now support local gateway ingress routing to redirect incoming traffic to an Elastic Network Interface (ENI) attached to an EC2 instance before traffic reaches workloads running on the Outpost; read Deploying Local Gateway Ingress Routing on AWS Outposts to learn more. Outposts racks now also support direct VPC routing to simplify the process of communicating with your on-premises network; read the What’s New to learn more.

Amazon SWF Console Experience Updated – The new console experience for Amazon Simple Workflow Service (SWF) gives you better visibility of your SWF domains along with additional information about your workflow executions and events. You can efficiently manage high-volume workloads and quickly find the detailed information that helps you to operate at peak efficiency. Read the What’s New to learn more.

Dynamic Intermediate Certificate Authorities – According to a post on the AWS Security Blog, public certificates issued through AWS Certificate Manager (ACM) will soon (October 11, 2022) be issued from one of several intermediate certificate authorities managed by Amazon. This change will be transparent to most customers and applications, except those that make use of certificate pinning. In some cases, older browsers will need to be updated in order to properly trust the Amazon Trust Services CAs.

X in Y – We launched existing AWS services and instance types in additional regions:

Other AWS News
AWS Open Source – Check out Installment #127 of the AWS Open Source News and Updates Newsletter to learn about new tools for AWS CloudFormation, AWS Lambda, Terraform / EKS, AWS Step Functions, AWS Identity and Access Management (IAM), and more.

New Case Study – Read this new case study to learn how the Deep Data Research Computing Center at Stanford University is creating tools designed to bridge the gap between biology and computer science in order to help researchers in precision medicine deliver tangible medical solutions.

Application Management – The AWS DevOps Blog showed you how to Implement Long-Running Deployments with AWS CloudFormation Custom Resources Using AWS Step Functions.

Architecture – The AWS Architecture Blog showed you how to Maintain Visibility Over the Use of Cloud Architecture Patterns.

Big Data – The AWS Big Data Blog showed you how to Optimize Amazon EMR Costs for Legacy and Spark Workloads.

Migration – In a two-part series on the AWS Compute Blog, Marcia showed you how to Lift and Shift a Web Application to AWS Serverless (Part 1, Part 2).

Mobile – The AWS Mobile Blog showed you how to Build Your Own Application for Route Optimization and Tracking using AWS Amplify and Amazon Location Service.

Security – The AWS Security Blog listed 10 Reasons to Import a Certificate into AWS Certificate Manager and 154 AWS Services that have achieved HITRUST Certificiation.

Training and Certification – The AWS Training and Certification Blog talked about The Value of Data and Pursuing the AWS Certified Data Analytics – Specialty Certification.

Containers – The AWS Containers Blog encouraged you to Achieve Consistent Application-Level Tagging for Cost Tracking in AWS.

Upcoming AWS Events
Check your calendar and sign up for an AWS event in your locale:

AWS Summits – Come together to connect, collaborate, and learn about AWS. Registration is open for the following in-person AWS Summits: Mexico City (September 21–22), Bogotá (October 4), and Singapore (October 6).

AWS Community Days – AWS Community Day events are community-led conferences to share and learn with one another. In September, the AWS community in the US will run events in Arlington, Virginia (September 30). In Europe, Community Day events will be held in October. Join us in Amersfoort, Netherlands (October 3), Warsaw, Poland (October 14), and Dresden, Germany (October 19).

AWS Fest – This third-party event will feature AWS influencers, community heroes, industry leaders, and AWS customers, all sharing AWS optimization secrets (September 29th), register here.

Stay Informed
I hope that you have enjoyed this look back at some of what took place in AWS-land last week! To better keep up with all of this news, please check out the following resources:

— Jeff;

New – Direct VPC Routing Between On-Premises Networks and AWS Outposts Rack

Post Syndicated from Steve Roberts original https://aws.amazon.com/blogs/aws/new-direct-vpc-routing-between-on-premises-networks-and-aws-outposts-rack/

Today, we announced direct VPC routing for AWS Outposts rack. This enables you to connect Outposts racks and on-premises networks using simplified IP address management. Direct VPC routing automatically advertises Amazon Virtual Private Cloud (Amazon VPC) subnet CIDR addresses to on-premises networks. This enables you to use the private IP addresses of resources in your VPC when communicating with your on-premises network. Furthermore, you can enable direct VPC routing using a self-serve process without needing to contact AWS.

AWS Outposts rack

If you’re unfamiliar, AWS Outposts rack, a part of the Outposts family, is a fully-managed service that offers the same AWS infrastructure, AWS services, APIs, and tools to virtually any on-premises datacenter or co-location space for a consistent hybrid experience. They’re ideal for workloads that require low-latency access to on-premises systems, local data processing, data residency, and migration of applications with local system interdependencies. Once installed, your Outposts rack becomes an extension of your VPC, and it’s managed using the same APIs, tools, and management controls that you already use in the cloud.

With direct VPC routing, you now have two options to configure and connect your Outposts rack to your on-premises networks. Previously, to configure network routing between an on-premises network and an Outposts rack, you needed to use Customer-owned IP addresses (CoIP). During an Outposts rack installation, this involved providing a separate IP address range/CIDR from your on-premises network for AWS to create an address pool, which is known as a CoIP pool. When an Amazon Elastic Compute Cloud (Amazon EC2) instance on your Outposts rack needed to communicate with your on-premises network, Outposts rack would perform a 1:1 network address translation (NAT) from the VPC private IP address to a CoIP address in the CoIP pool. Using CoIP means that you must manage both VPC and CoIP address pools, without overlap, and configure route propagation between the two sets of addresses. When adding a subnet to a VPC, you also needed to follow several steps to update route propagation between your networks to recognize the new subnet addresses.

Managing IP address ranges for AWS cloud and onsite resources, as well as dealing with CoIP ranges on Outposts rack, can be an operational burden. Although the option to use CoIP is still available and will continue to be fully supported, the new direct VPC routing option simplifies your IP address management. Automatic advertisement of CIDR addresses for subnets, including new subnets added in the future, between the VPC and your Outposts rack, removes the need for you to reconfigure IP addresses. This also keeps route propagation up-to-date, thereby saving you time and effort. Furthermore, as mentioned earlier, you can enable all of this with a self-serve option.

Enabling Direct VPC Routing
You can select either CoIP or direct VPC routing approaches and utilize a new self-service API, CreateLocalGatewayRouteTable, to configure direct VPC routing for both new and existing Outposts racks. This eliminates the need to contact AWS to enable the configuration. To enable direct VPC routing, simply set the mode property in the CreateLocalGatewayRouteTable API’s request parameters to the value direct-vpc-routing. If you’re already using CoIP, then you must delete and recreate the route table that’s propagating traffic between the Outposts rack and your on-premises network.

The following example diagram, taken from the user guide, illustrates the setup for an Outposts rack running several Amazon EC2 instances and connected to an on-premises network, with automatic address advertisement. Note that private IP address ranges are utilized across the Outposts rack resources and the on-premises network.

Example of direct VPC routing

Get started with Direct VPC Routing today
The option to enable direct VPC routing is available now for both new and existing Outposts racks. As mentioned earlier, the option to use CoIP will continue to be supported, but now you can choose between direct VPC routing and CoIP based on your on-premises networking needs. Direct VPC routing is available in all AWS Regions where Outposts rack is supported.

Find more information on this topic in the AWS Outposts User Guide. More information on AWS Outposts rack is available here.

— Steve

AWS Week In Review – September 12, 2022

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/aws-week-in-review-september-12-2022/

I am working from London, UK, this week to record sessions for the upcoming Innovate EMEA online conference—more about this in a future Week In Review. While I was crossing the channel, I took the time to review what happened on AWS last week.

Last Week’s Launches
Here are some launches that got my attention:

Seekable OCI for lazy loading container images. Seekable OCI (SOCI) is a technology open sourced by AWS that enables containers to launch faster by lazily loading the container image. SOCI works by creating an index of the files within an existing container image. This index is a key enabler to launching containers faster, providing the capability to extract an individual file from a container image before downloading the entire archive. Check out the source code on GitHub.

Amazon Lookout for Metrics now lets you filter data by dimensions and increased the limits on the number of measures and dimensions. Lookout for Metrics uses machine learning (ML) to automatically detect and diagnose anomalies (i.e., outliers from the norm) in business and operational data, such as a sudden dip in sales revenue or customer acquisition rates.

Amazon SageMaker has three new capabilities. First, SageMaker Canvas added additional capabilities to explore and analyze data with advanced visualizations. Second, SageMaker Studio now sends API user identity data to AWS CloudTrail. And third, SageMaker added TensorFlow image classification to its list of builtin algorithms.

The AWS console launches a widget to display the most recent AWS blog posts on the console landing page. Being part of the AWS News Blog team, I couldn’t be more excited about a launch this week. 😀

AW Console Blog widget

Other AWS News
Some other updates and news that you may have missed:

The Amazon Science blog published an article on the design of a pinch grasping robot. It is one of the many areas where we try to improve the efficiency of our fulfillment centers. A must-read if you’re into robotics or logistics.

The Public Sector blog has an article on how Satellogic and AWS are harnessing the power of space and cloud. Satellogic is creating a live catalog of Earth and delivering daily updates to create a complete picture of changes to our planet for decision-makers. Satellogic is generating massive volumes of data, with each of its satellites collecting an average of 50GB of data daily. They are using compute, storage, analytics, and ground station infrastructure in support of their growth.

Event Ruler is now open-source. Talking about open-source, the source code of the core rule engine built first for Amazon CloudWatch Events, and now the core of Amazon Event Bridge, is newly available on GitHub. This is a Java library that allows applications to identify events that match a set of rules. Events and rules are expressed as JSON documents. Rules are compiled for fast evaluation by a finite state engine. Read the announcement blog post to understand how Event Bridge works under the hood.

HP Anyware (formerly Teradici CAS) is now available for Amazon EC2 Mac instances, from the AWS Marketplace. HP Anyware is a remote access solution that provides pixel-perfect rendering for your remote Mac Mini running in the AWS cloud. It uses PCoIP™ to securely and efficiently access the remote macOS machines. You can connect from anywhere, using a PCoIP client application or from thin terminals such as Thin Clients or Zero Clients workstations.

Upcoming AWS Events
Check your calendars and sign up for these AWS events that are happening all over the world:

AWS Summits – Come together to connect, collaborate, and learn about AWS. Registration is open for the following in-person AWS Summits: Mexico City (September 21–22), Bogotá (October 4), and Singapore (October 6).

AWS Community Days – AWS Community Day events are community-led conferences to share and learn with one another. In September, the AWS community in the US will run events in Arlington, Virginia (September 30). In Europe, Community Day events will be held in October. Join us in Amersfoort, Netherlands (October 3), Warsaw, Poland (October 14), and Dresden, Germany (October 19).

That’s all from me for this week. Come back next Monday for another Week in Review!

— seb

 

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

AWS Week in Review – September 5, 2022

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/aws-week-in-review-september-5-2022/

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

As a new week begins, let’s quickly look back at the most significant AWS news from the previous seven days.

Last Week’s Launches
Here are the launches that got my attention last week:

AWS announces open-sourced credentials-fetcher to simplify Microsoft AD access from Linux containers. You can find more in the What’s New post.

AWS Step Functions now has 14 new intrinsic functions that help you process data more efficiently and make it easier to perform data processing tasks such as array manipulation, JSON object manipulation, and math functions within your workflows without having to invoke downstream services or add Task states.

AWS SAM CLI esbuild support is now generally available. You can now use esbuild in the SAM CLI build workflow for your JavaScript applications.

Amazon QuickSight launches a new user interface for dataset management that replaces the existing popup dialog modal with a full-page experience, providing a clearer breakdown of dataset management categories.

AWS GameKit adds Unity support. With this release for Unity, you can integrate cloud-based game features into Win64, MacOS, Android, or iOS games from both the Unreal and Unity engines with just a few clicks.

AWS and VMware announce VMware Cloud on AWS integration with Amazon FSx for NetApp ONTAP. Read more in Veliswa‘s blog post.

The AWS Region in the United Arab Emirates (UAE) is now open. More info in Marcia‘s blog post.

View of Abu Dhabi in the United Arab Emirates

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
A few more blog posts you might have missed:

Easy analytics and cost-optimization with Amazon Redshift Serverless – Four different use cases of Redshift Serverless are discussed in this post.

Building cost-effective AWS Step Functions workflows – In this blog post, Ben explains the difference between Standard and Express Workflows, including costs, migrating from Standard to Express, and some interesting ways of using both together.

How to subscribe to the new Security Hub Announcements topic for Amazon SNS – You can now receive updates about new Security Hub services and features, newly supported standards and controls, and other Security Hub changes.

Deploying AWS Lambda functions using AWS Controllers for Kubernetes (ACK) – With the ACK service controller for AWS Lambda, you can provision and manage Lambda functions with kubectl and custom resources.

For AWS open-source news and updates, here’s the latest newsletter curated by Ricardo to bring you the most recent updates on open-source projects, posts, events, and more.

Upcoming AWS Events
Depending on where you are on this planet, there are many opportunities to meet and learn:

AWS Summits – Come together to connect, collaborate, and learn about AWS. Registration is open for the following in-person AWS Summits: Ottawa (September 8), New Delhi (September 9), Mexico City (September 21–22), Bogotá (October 4), and Singapore (October 6).

AWS Community Days – AWS Community Day events are community-led conferences to share and learn with one another. In September, the AWS community in the US will run events in the Bay Area, California (September 9) and Arlington, Virginia (September 30). In Europe, Community Day events will be held in October. Join us in Amersfoort, Netherlands (October 3), Warsaw, Poland (October 14), and Dresden, Germany (October 19).

That’s all from me for this week. Come back next Monday for another Week in Review!

— Danilo