All posts by Veliswa Boya

AWS Weekly Roundup: New features on Knowledge Bases for Amazon Bedrock, OAC for Lambda function URL origins on Amazon CloudFront, and more (April 15, 2024)

Post Syndicated from Veliswa Boya original

AWS Community Days conferences are in full swing with AWS communities around the globe. The AWS Community Day Poland was hosted last week with more than 600 cloud enthusiasts in attendance. Community speakers Agnieszka Biernacka, Krzysztof Kąkol, and more, presented talks which captivated the audience and resulted in vibrant discussions throughout the day. My teammate, Wojtek Gawroński, was at the event and he’s already looking forward to attending again next year!

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

Amazon CloudFront now supports Origin Access Control (OAC) for Lambda function URL origins – Now you can protect your AWS Lambda URL origins by using Amazon CloudFront Origin Access Control (OAC) to only allow access from designated CloudFront distributions. The CloudFront Developer Guide has more details on how to get started using CloudFront OAC to authenticate access to Lambda function URLs from your designated CloudFront distributions.

AWS Client VPN and AWS Verified Access migration and interoperability patterns – If you’re using AWS Client VPN or a similar third-party VPN-based solution to provide secure access to your applications today, you’ll be pleased to know that you can now combine the use of AWS Client VPN and AWS Verified Access for your new or existing applications.

These two announcements related to Knowledge Bases for Amazon Bedrock caught my eye:

Metadata filtering to improve retrieval accuracy – With metadata filtering, you can retrieve not only semantically relevant chunks but a well-defined subset of those relevant chunks based on applied metadata filters and associated values.

Custom prompts for the RetrieveAndGenerate API and configuration of the maximum number of retrieved results – These are two new features which you can now choose as query options alongside the search type to give you control over the search results. These are retrieved from the vector store and passed to the Foundation Models for generating the answer.

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

Other AWS news
AWS open source news and updates – My colleague Ricardo writes this weekly open source newsletter in which he highlights new open source projects, tools, and demos from the AWS Community.

Upcoming AWS events
AWS Summits – These are free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Whether you’re in the Americas, Asia Pacific & Japan, or EMEA region, learn here about future AWS Summit events happening in your area.

AWS Community Days – Join an AWS Community Day event just like the one I mentioned at the beginning of this post to participate in technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from your area. If you’re in Kenya, or Nepal, there’s an event happening in your area this coming weekend.

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

That’s all for this week. Check back next Monday for another Weekly Roundup!

– Veliswa

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

Tackle complex reasoning tasks with Mistral Large, now available on Amazon Bedrock

Post Syndicated from Veliswa Boya original

Last month, we announced the availability of two high-performing Mistral AI models, Mistral 7B and Mixtral 8x7B on Amazon Bedrock. Mistral 7B, as the first foundation model of Mistral, supports English text generation tasks with natural coding capabilities. Mixtral 8x7B is a popular, high-quality, sparse Mixture-of-Experts (MoE) model, that is ideal for text summarization, question and answering, text classification, text completion, and code generation.

Today, we’re announcing the availability of Mistral Large on Amazon Bedrock. Mistral Large is ideal for complex tasks that require substantial reasoning capabilities, or ones that are highly specialized, such as Synthetic Text Generation or Code Generation.

What you need to know about Mistral Large:

  • It’s natively fluent in English, French, Spanish, German, and Italian, with a nuanced understanding of grammar and cultural context.
  • It has a 32K token context window allows precise information recall from large documents.
  • Its precise instruction-following enables you to design your moderation policies – the folks at Mistral AI used it to set up the system-level moderation of their beta assistant demonstrator le Chat. Your first interaction with Large Language Models (LLMs) revolves around prompts. The art of crafting effective prompts is essential for generating desirable responses from LLMs and Mistral AI has a guide with example prompts showing different prompting capabilities.

Getting started with Mistral Large
To get started with Mistral Large on Bedrock, you first need to get access to the model. On the Amazon Bedrock console, select Model access, and then select Manage model access. Next, select Mistral Large, and then select Save changes.


Once you have access to Mistral Large, you can use the model on Bedrock. Refresh the base models table to view the updated status.

To see the complex reasoning aspect of Mistral Large in action I tried the following prompt in the Playgrounds section:



Calculate the difference in payment dates between the two customers whose payment amounts are closest to each other in the given dataset:



To find the difference in payment dates between the two customers whose payment amounts are closest to each other, we first need to identify the two closest payment amounts and then find their respective payment dates.

The payment amounts are: 125.5, 89.99, 120.0, 54.3, 210.2

The two closest payment amounts are 125.5 and 120.0. The payment date for the payment amount 125.5 is '2021-10-05' and for the payment amount 120.0 is '2021-10-07'.

The difference in payment dates can be calculated as follows:

Date difference = |'2021-10-07' - '2021-10-05'| = 2 days

So, the difference in payment dates between the two customers whose payment amounts are closest to each other is 2 days.

Earlier in this post I mentioned that Mistral Large is natively fluent in several languages, with a nuanced understanding of grammar and cultural context. I tested the model’s understanding of the French language:


Lequel est le plus lourd une livre de fer ou un kilogramme de plume


Un kilogramme de plumes est plus lourd qu'une livre de fer.

Pour comprendre cela, il est important de se rappeler que :

1. Une livre (lb) est une unité de mesure de masse principalement utilisée aux États-Unis et dans d'autres systèmes de mesure impériaux, tandis qu'un kilogramme (kg) est l'unité de base de masse dans le système international d'unités (SI).

2. 1 kilogramme est approximativement égal à 2,2 livres.

Donc, un kilogramme de plumes est plus lourd qu'une livre de fer, car il correspond à environ 2,2 livres de plumes.

Programmatically interact with Mistral Large
You can also use AWS Command Line Interface (CLI) and AWS Software Development Kit (SDK) to make various calls using Amazon Bedrock APIs. Following, is a sample code in Python that interacts with Amazon Bedrock Runtime APIs with AWS SDK. If you specify in the prompt that “You will only respond with a JSON object with the key X, Y, and Z.”, you can use JSON format output in easy downstream tasks:

import boto3
import json

bedrock = boto3.client(service_name="bedrock-runtime", region_name='us-east-1')

prompt = """
<s>[INST]You are a summarization system that can provide summaries with associated confidence 
scores. In clear and concise language, provide three short summaries of the following essay, 
along with their confidence scores. You will only respond with a JSON object with the key Summary 
and Confidence. Do not provide explanations.[/INST]

# Essay: 
The generative artificial intelligence (AI) revolution is in full swing, and customers of all sizes and across industries are taking advantage of this transformative technology to reshape their businesses. From reimagining workflows to make them more intuitive and easier to enhancing decision-making processes through rapid information synthesis, generative AI promises to redefine how we interact with machines. It’s been amazing to see the number of companies launching innovative generative AI applications on AWS using Amazon Bedrock. Siemens is integrating Amazon Bedrock into its low-code development platform Mendix to allow thousands of companies across multiple industries to create and upgrade applications with the power of generative AI. Accenture and Anthropic are collaborating with AWS to help organizations—especially those in highly-regulated industries like healthcare, public sector, banking, and insurance—responsibly adopt and scale generative AI technology with Amazon Bedrock. This collaboration will help organizations like the District of Columbia Department of Health speed innovation, improve customer service, and improve productivity, while keeping data private and secure. Amazon Pharmacy is using generative AI to fill prescriptions with speed and accuracy, making customer service faster and more helpful, and making sure that the right quantities of medications are stocked for customers.

To power so many diverse applications, we recognized the need for model diversity and choice for generative AI early on. We know that different models excel in different areas, each with unique strengths tailored to specific use cases, leading us to provide customers with access to multiple state-of-the-art large language models (LLMs) and foundation models (FMs) through a unified service: Amazon Bedrock. By facilitating access to top models from Amazon, Anthropic, AI21 Labs, Cohere, Meta, Mistral AI, and Stability AI, we empower customers to experiment, evaluate, and ultimately select the model that delivers optimal performance for their needs.

Announcing Mistral Large on Amazon Bedrock
Today, we are excited to announce the next step on this journey with an expanded collaboration with Mistral AI. A French startup, Mistral AI has quickly established itself as a pioneering force in the generative AI landscape, known for its focus on portability, transparency, and its cost-effective design requiring fewer computational resources to run. We recently announced the availability of Mistral 7B and Mixtral 8x7B models on Amazon Bedrock, with weights that customers can inspect and modify. Today, Mistral AI is bringing its latest and most capable model, Mistral Large, to Amazon Bedrock, and is committed to making future models accessible to AWS customers. Mistral AI will also use AWS AI-optimized AWS Trainium and AWS Inferentia to build and deploy its future foundation models on Amazon Bedrock, benefitting from the price, performance, scale, and security of AWS. Along with this announcement, starting today, customers can use Amazon Bedrock in the AWS Europe (Paris) Region. At launch, customers will have access to some of the latest models from Amazon, Anthropic, Cohere, and Mistral AI, expanding their options to support various use cases from text understanding to complex reasoning.

Mistral Large boasts exceptional language understanding and generation capabilities, which is ideal for complex tasks that require reasoning capabilities or ones that are highly specialized, such as synthetic text generation, code generation, Retrieval Augmented Generation (RAG), or agents. For example, customers can build AI agents capable of engaging in articulate conversations, generating nuanced content, and tackling complex reasoning tasks. The model’s strengths also extend to coding, with proficiency in code generation, review, and comments across mainstream coding languages. And Mistral Large’s exceptional multilingual performance, spanning French, German, Spanish, and Italian, in addition to English, presents a compelling opportunity for customers. By offering a model with robust multilingual support, AWS can better serve customers with diverse language needs, fostering global accessibility and inclusivity for generative AI solutions.

By integrating Mistral Large into Amazon Bedrock, we can offer customers an even broader range of top-performing LLMs to choose from. No single model is optimized for every use case, and to unlock the value of generative AI, customers need access to a variety of models to discover what works best based for their business needs. We are committed to continuously introducing the best models, providing customers with access to the latest and most innovative generative AI capabilities.

“We are excited to announce our collaboration with AWS to accelerate the adoption of our frontier AI technology with organizations around the world. Our mission is to make frontier AI ubiquitous, and to achieve this mission, we want to collaborate with the world’s leading cloud provider to distribute our top-tier models. We have a long and deep relationship with AWS and through strengthening this relationship today, we will be able to provide tailor-made AI to builders around the world.”

– Arthur Mensch, CEO at Mistral AI.

Customers appreciate choice
Since we first announced Amazon Bedrock, we have been innovating at a rapid clip—adding more powerful features like agents and guardrails. And we’ve said all along that more exciting innovations, including new models will keep coming. With more model choice, customers tell us they can achieve remarkable results:

“The ease of accessing different models from one API is one of the strengths of Bedrock. The model choices available have been exciting. As new models become available, our AI team is able to quickly and easily evaluate models to know if they fit our needs. The security and privacy that Bedrock provides makes it a great choice to use for our AI needs.”

– Jamie Caramanica, SVP, Engineering at CS Disco.

“Our top priority today is to help organizations use generative AI to support employees and enhance bots through a range of applications, such as stronger topic, sentiment, and tone detection from customer conversations, language translation, content creation and variation, knowledge optimization, answer highlighting, and auto summarization. To make it easier for them to tap into the potential of generative AI, we’re enabling our users with access to a variety of large language models, such as Genesys-developed models and multiple third-party foundational models through Amazon Bedrock, including Anthropic’s Claude, AI21 Labs’s Jurrassic-2, and Amazon Titan. Together with AWS, we’re offering customers exponential power to create differentiated experiences built around the needs of their business, while helping them prepare for the future.”

– Glenn Nethercutt, CTO at Genesys.

As the generative AI revolution continues to unfold, AWS is poised to shape its future, empowering customers across industries to drive innovation, streamline processes, and redefine how we interact with machines. Together with outstanding partners like Mistral AI, and with Amazon Bedrock as the foundation, our customers can build more innovative generative AI applications.

Democratizing access to LLMs and FMs
Amazon Bedrock is democratizing access to cutting-edge LLMs and FMs and AWS is the only cloud provider to offer the most popular and advanced FMs to customers. The collaboration with Mistral AI represents a significant milestone in this journey, further expanding Amazon Bedrock’s diverse model offerings and reinforcing our commitment to empowering customers with unparalleled choice through Amazon Bedrock. By recognizing that no single model can optimally serve every use case, AWS has paved the way for customers to unlock the full potential of generative AI. Through Amazon Bedrock, organizations can experiment with and take advantage of the unique strengths of multiple top-performing models, tailoring their solutions to specific needs, industry domains, and workloads. This unprecedented choice, combined with the robust security, privacy, and scalability of AWS, enables customers to harness the power of generative AI responsibly and with confidence, no matter their industry or regulatory constraints.

body = json.dumps({
    "prompt": prompt,
    "max_tokens": 512,
    "top_p": 0.8,
    "temperature": 0.5,

modelId = "mistral.mistral-large-2402-v1:0"

accept = "application/json"
contentType = "application/json"

response = bedrock.invoke_model(


You can get JSON formatted output as like:

   "Summaries": [ 
         "Summary": "The author discusses their early experiences with programming and writing, 
starting with writing short stories and programming on an IBM 1401 in 9th grade. 
They then moved on to working with microcomputers, building their own from a Heathkit, 
and eventually convincing their father to buy a TRS-80 in 1980. They wrote simple games, 
a program to predict rocket flight trajectories, and a word processor.", 
         "Confidence": 0.9 
         "Summary": "The author began college as a philosophy major, but found it to be unfulfilling 
and switched to AI. They were inspired by a novel and a PBS documentary, as well as the 
potential for AI to create intelligent machines like those in the novel. Despite this 
excitement, they eventually realized that the traditional approach to AI was flawed and 
shifted their focus to Lisp.", 
         "Confidence": 0.85 
         "Summary": "The author briefly worked at Interleaf, where they found that their Lisp skills 
were highly valued. They eventually left Interleaf to return to RISD, but continued to work 
as a freelance Lisp hacker. While at RISD, they started painting still lives in their bedroom 
at night, which led to them applying to art schools and eventually attending the Accademia 
di Belli Arti in Florence.", 
         "Confidence": 0.9 

To learn more prompting capabilities in Mistral AI models, visit Mistral AI documentation.

Now Available
Mistral Large, along with other Mistral AI models (Mistral 7B and Mixtral 8x7B), is available today on Amazon Bedrock in the US East (N. Virginia), US West (Oregon), and Europe (Paris) Regions; check the full Region list for future updates.

Share and learn with our generative AI community at Give Mistral Large a try in the Amazon Bedrock console today and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

Read about our collaboration with Mistral AI and what it means for our customers.


AWS Weekly Roundup — .Net Runtime for AWS Lambda, PartyRock Hackathon, and more — February 26, 2024

Post Syndicated from Veliswa Boya original

The Community AWS re:invent 2023 re:caps continue! Recently, I was invited to participate in one of these events hosted by the AWS User Group Kenya, and was able to learn and spend time with this amazing community.

AWS User Group Kenya

AWS User Group Kenya

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

.NET 8 runtime for AWS Lambda – AWS Lambda now supports .NET 8 as both a managed runtime and container base image. This support provides you with .NET 8 features that include API enhancements, improved Native Ahead of Time (Native AOT) support, and improved performance. .NET 8 supports C# 12, F# 8, and PowerShell 7.4. You can develop Lambda functions in .NET 8 using the AWS Toolkit for Visual Studio, the AWS Extensions for .NET CLI, AWS Serverless Application Model (AWS SAM), AWS CDK, and other infrastructure as code tools.

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 projects, programs, and news items that you might find interesting:

Earlier this month, I used this image to call attention to the PartyRock Hackathon that’s currently in progress. The deadline to join the hackathon is fast approaching so be sure to signup before time runs out.

Amazon API Gateway – Amazon API Gateway processed over 100 trillion API requests in 2023, and we continue to see growing demand for API-driven applications. API Gateway is a fully-managed service that enables you to create, publish, maintain, monitor, and secure APIs at any scale. Customers that onboarded large workloads on API Gateway in 2023 told us they chose the service for its availability, security, and serverless architecture. Those in regulated industries value API Gateway’s private endpoints, which are isolated from the public internet and only accessible from your Amazon Virtual Private Cloud (VPC).

AWS open source news and updates – My colleague Ricardo writes this weekly open source newsletter in which he highlights new open source projects, tools, and demos from the AWS Community.

Upcoming AWS events
Season 3 of the Build on Generative AI Twitch show has kicked off. Join every Monday on Twitch at 9AM PST/Noon EST/18h CET to learn among others, how you can build generative AI-enabled applications.

If you’re in the EMEA timezone, there is still time to register and watch the AWS Innovate Online Generative AI & Data Edition taking place on February 29. Innovate Online events are free, online, and designed to inspire and educate you about building on AWS. Whether you’re in the Americas, Asia Pacific & Japan, or EMEA region, learn here about future AWS Innovate Online events happening in your timezone.

AWS Community re:Invent re:Caps – Join a Community re:Cap event organized by volunteers from AWS User Groups and AWS Cloud Clubs around the world to learn about the latest announcements from AWS re:Invent.

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

That’s all for this week. Check back next Monday for another Weekly Roundup!


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

AWS Weekly Roundup — Amazon Q in AWS Glue, Amazon PartyRock Hackathon, CDK Migrate, and more — February 5, 2024

Post Syndicated from Veliswa Boya original

With all the generative AI announcements at AWS re:invent 2023, I’ve committed to dive deep into this technology and learn as much as I can. If you are too, I’m happy that among other resources available, the AWS community also has a space that I can access for generative AI tools and guides.

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

Amazon Q data integration in AWS Glue (Preview) – Now you can use natural language to ask Amazon Q to author jobs, troubleshoot issues, and answer questions about AWS Glue and data integration. Amazon Q was launched in preview at AWS re:invent 2023, and is a generative AI–powered assistant to help you solve problems, generate content, and take action.

General availability of CDK Migrate – CDK Migrate is a component of the AWS Cloud Development Kit (CDK) that enables you to migrate AWS CloudFormation templates, previously deployed CloudFormation stacks, or resources created outside of Infrastructure as Code (IaC) into a CDK application. This feature was launched alongside the CloudFormation IaC Generator to give you an end-to-end experience that enables you to create an IaC configuration based off a resource, as well as its relationships. You can expect the IaC generator to have a huge impact for a common use case we’ve seen.

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 projects, programs, and news items that you might find interesting:

Amazon API Gateway processed over 100 trillion API requests in 2023, demonstrating the growing demand for API-driven applications. API Gateway is a fully-managed API management service. Customers from all industry verticals told us they’re adopting API Gateway for multiple reasons. First, its ability to scale to meet the demands of even the most high-traffic applications. Second, its fully-managed, serverless architecture, which eliminates the need to manage any infrastructure, and frees customers to focus on their core business needs.

Join the PartyRock Generative AI Hackathon by AWS. This is a challenge for you to get hands-on building generative AI-powered apps. You’ll use Amazon PartyRock, an Amazon Bedrock Playground, as a fast and fun way to learn about Prompt Engineering and Foundational Models (FMs) to build a functional app with generative AI.

AWS open source news and updates – My colleague Ricardo writes this weekly open source newsletter in which he highlights new open source projects, tools, and demos from the AWS Community.

Upcoming AWS events
Whether you’re in the Americas, Asia Pacific & Japan, or EMEA region, there’s an upcoming AWS Innovate Online event that fits your timezone. Innovate Online events are free, online, and designed to inspire and educate you about AWS.

AWS Summits are a series of free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. These events are designed to educate you about AWS products and services and help you develop the skills needed to build, deploy, and operate your infrastructure and applications. Find an AWS Summit near you and register or set a notification to know when registration opens for a Summit that interests you.

AWS Community re:Invent re:Caps – Join a Community re:Cap event organized by volunteers from AWS User Groups and AWS Cloud Clubs around the world to learn about the latest announcements from AWS re:Invent.

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

That’s all for this week. Check back next Monday for another Weekly Roundup!


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

Amazon CloudWatch Application Signals for automatic instrumentation of your applications (preview)

Post Syndicated from Veliswa Boya original

One of the challenges with distributed systems is that they are made up of many interdependent services, which add a degree of complexity when you are trying to monitor their performance. Determining which services and APIs are experiencing high latencies or degraded availability requires manually putting together telemetry signals. This can result in time and effort establishing the root cause of any issues with the system due to the inconsistent experiences across metrics, traces, logs, real user monitoring, and synthetic monitoring.

You want to provide your customers with continuously available and high-performing applications. At the same time, the monitoring that assures this must be efficient, cost-effective, and without undifferentiated heavy lifting.

Amazon CloudWatch Application Signals helps you automatically instrument applications based on best practices for application performance. There is no manual effort, no custom code, and no custom dashboards. You get a pre-built, standardized dashboard showing the most important metrics, such as volume of requests, availability, latency, and more, for the performance of your applications. In addition, you can define Service Level Objectives (SLOs) on your applications to monitor specific operations that matter most to your business. An example of an SLO could be to set a goal that a webpage should render within 2000 ms 99.9 percent of the time in a rolling 28-day interval.

Application Signals automatically correlates telemetry across metrics, traces, logs, real user monitoring, and synthetic monitoring to speed up troubleshooting and reduce application disruption. By providing an integrated experience for analyzing performance in the context of your applications, Application Signals gives you improved productivity with a focus on the applications that support your most critical business functions.

My personal favorite is the collaboration between teams that’s made possible by Application Signals. I started this post by mentioning that distributed systems are made up of many interdependent services. On the Service Map, which we will look at later in this post, if you, as a service owner, identify an issue that’s caused by another service, you can send a link to the owner of the other service to efficiently collaborate on the triage tasks.

Getting started with Application Signals
You can easily collect application and container telemetry when creating new Amazon EKS clusters in the Amazon EKS console by enabling the new Amazon CloudWatch Observability EKS add-on. Another option is to enable for existing Amazon EKS Clusters or other compute types directly in the Amazon CloudWatch console.

Create service map

After enabling Application Signals via the Amazon EKS add-on or Custom option for other compute types, Application Signals automatically discovers services and generates a standard set of application metrics such as volume of requests and latency spikes or availability drops for APIs and dependencies, to name a few.

Specify platform

All of the services discovered and their golden metrics (volume of requests, latency, faults and errors) are then automatically displayed on the Services page and the Service Map. The Service Map gives you a visual deep dive to evaluate the health of a service, its operations, dependencies, and all the call paths between an operation and a dependency.

Auto-generated map

The list of services that are enabled in Application Signals will also show in the services dashboard, along with operational metrics across all of your services and dependencies to easily spot anomalies. The Application column is auto-populated if the EKS cluster belongs to an application that’s tagged in AppRegistry. The Hosted In column automatically detects which EKS pod, cluster, or namespace combination the service requests are running in, and you can select one to go directly to Container Insights for detailed container metrics such as CPU or memory utilization, to name a few.

Team collaboration with Application Signals
Now, to expand on the team collaboration that I mentioned at the beginning of this post. Let’s say you consult the services dashboard to do sanity checks and you notice two SLO issues for one of your services named pet-clinic-frontend. Your company maintains a set of SLOs, and this is the view that you use to understand how the applications are performing against the objectives. For the services that are tagged in AppRegistry all teams have a central view of the definition and ownership of the application. Further navigation to the service map gives you even more details on the health of this service.

At this point you make the decision to send the link to thepet-clinic-frontendservice to Sarah whose details you found in the AppRegistry. Sarah is the person on-call for this service. The link allows you to efficiently collaborate with Sarah because it’s been curated to land directly on the triage view that is contextualized based on your discovery of the issue. Sarah notices that the POST /api/customer/owners latency has increased to 2k ms for a number of requests and as the service owner, dives deep to arrive at the root cause.

Clicking into the latency graph returns a correlated list of traces that correspond directly to the operation, metric, and moment in time, which helps Sarah to find the exact traces that may have led to the increase in latency.

Sarah uses Amazon CloudWatch Synthetics and Amazon CloudWatch RUM and has enabled the X-Ray active tracing integration to automatically see the list of relevant canaries and pages correlated to the service. This integrated view now helps Sarah gain multiple perspectives in the performance of the application and quickly troubleshoot anomalies in a single view.

Available now
Amazon CloudWatch Application Signals is available in preview and you can start using it today in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Ireland), Asia Pacific (Sydney), and Asia Pacific (Tokyo).

To learn more, visit the Amazon CloudWatch user guide. You can submit your questions to AWS re:Post for Amazon CloudWatch, or through your usual AWS Support contacts.


New generative AI features in Amazon Connect, including Amazon Q, facilitate improved contact center service

Post Syndicated from Veliswa Boya original

If you manage a contact center, then you know the critical role that agents play in helping your organization build customer trust and loyalty. Those of us who’ve reached out to a contact center know how important agents are with guiding through complex decisions and providing fast and accurate solutions where needed. This can take time, and if not done correctly, then it may lead to frustration.

Generative AI capabilities in Amazon Connect
Today, we’re announcing that the existing artificial intelligence (AI) features of Amazon Connect now have generative AI capabilities that are powered by large language models (LLMs) available through Amazon Bedrock to transform how contact centers provide service to customers. LLMs are pre-trained on vast amounts of data, commonly known as foundation models (FMs), and they can understand and learn, generate text, engage in interactive conversations, answer questions, summarize dialogs and documents, and provide recommendations.

Amazon Q in Connect: recommended responses and actions for faster customer support
Organizations are in a state of constant change. To maintain a high level of performance that keeps up with these organizational changes, contact centers continuously onboard, train, and coach agents. Even with training and coaching, agents must often search through different sources of information, such as product guides and organization policies, to provide exceptional service to customers. This can increase customer wait times, lowering customer satisfaction and increasing contact center costs.

Amazon Q in Connect, a generative AI-powered agent assistant that includes functionality formerly available as Amazon Connect Wisdom, understands customer intents and uses relevant sources of information to deliver accurate responses and actions for the agent to communicate and resolve unique customer needs, all in real-time. Try Amazon Q in Connect for no charge until March 1, 2024. The feature is easy to enable, and you can get started in the Amazon Connect console.

Amazon Connect Contact Lens: generative post-contact summarization for increased productivity
To improve customer interactions and make sure details are available for future reference, contact center managers rely on the notes that agents manually create after every customer interaction. These notes include details on how a customer issue was addressed, key moments of the conversation, and any pending follow-up items.

Amazon Connect Contact Lens now provides generative AI-powered post-contact summarization, and enables contact center managers to more efficiently monitor and help improve contact quality and agent performance. For example, you can use summaries to track commitments made to customers and make sure of the prompt completion of follow-up actions. Moments after a customer interaction, Contact Lens now condenses the conversation into a concise and coherent summary.

Amazon Lex in Amazon Connect: assisted slot resolution
Using Amazon Lex, you can already build chatbots, virtual agents, and interactive voice response (IVR) which lets your customers schedule an appointment without speaking to a human agent. For example, “I need to change my travel reservation for myself and my two children,” might be difficult for a traditional bot to resolve to a numeric value (how many people are on the travel reservation?).

With the new assisted slot resolution feature, Amazon Lex can now resolve slot values in user utterances with great accuracy (for example, providing an answer to the previous question by providing a correct numeric value of three). This is powered by the advanced reasoning capabilities of LLMs which improve accuracy and provide a better customer experience. Learn about all the features of Amazon Lex, including the new generative AI-powered capabilities to help you build better self-service experiences.

Amazon Connect Customer Profiles: quicker creation of unified customer profiles for personalized customer experiences
Customers expect personalized customer service experiences. To provide this, contact centers need a comprehensive understanding of customers’ preferences, purchases, and interactions. To achieve that, contact center administrators create unified customer profiles by merging customer data from a number of applications. These applications each have different types of customer data stored in varied formats across a range of data stores. Stitching together data from these various data stores needs contact center administrators to understand their data and figure out how to organize and combine it into a unified format. To accomplish this, they spend weeks compiling unified customer profiles.

Starting today, Amazon Connect Customer Profiles uses LLMs to shorten the time needed to create unified customer profiles. When contact center administrators add data sources such as Amazon Simple Storage Service (Amazon S3), Adobe Analytics, Salesforce, ServiceNow, and Zendesk, Customer Profiles analyze the data to understand what the data format and content represents and how the data relates to customers’ profiles. Then, Customer Profiles then automatically determines how to organize and combine data from different sources into complete, accurate profiles. With just a few steps, managers can review, make any necessary edits, and complete the setup of customer profiles.

Review summary mapping

In-app, web, and video capabilities in Amazon Connect
As an organization, you want to provide great, easy-to-use, and convenient customer service. Earlier in this post I talked about self-service chatbots and how they help you with this. At times customers want to move beyond the chatbot, and beyond an audio conversation with the agent.

Amazon Connect now has in-app, web, and video capabilities to help you deliver rich, personalized customer experiences (see Amazon Lex features for details). Using the fully-managed communication widget, and with a few lines of code, you can implement these capabilities on your web and mobile applications. This allows your customers to get support from a web or mobile application without ever having to leave the page. Video can be enabled by either the agent only, by the customer only, or by both agent and customer.

Video calling

Amazon Connect SMS: two-way SMS capabilities
Almost everyone owns a mobile device and we love the flexibility of receiving text-based support on-the-go. Contact center leaders know this, and in the past have relied on disconnected, third-party solutions to provide two-way SMS to customers.

Amazon Connect now has two-way SMS capabilities to enable contact center leaders to provide this flexibility (see Amazon Lex features for details). This improves customer satisfaction and increases agent productivity without costly integration with third-party solutions. SMS chat can be enabled using the same configuration, Amazon Connect agent workspace, and analytics as calls and chats.

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Amazon EBS Snapshots Archive is now available with AWS Backup

Post Syndicated from Veliswa Boya original

Today we announce the availability of Amazon Elastic Block Store (Amazon EBS) Snapshots Archive with AWS Backup. Previously available only in the Amazon EC2 console or Amazon Data Lifecycle Manager, this feature gives you the ability to transition your infrequently accessed Amazon EBS Snapshots to low-cost archive, long-term storage of your rarely-accessed snapshots that do not need frequent or fast retrieval.

Amazon EBS Snapshots Archive in the AWS Backup console
Snapshots Archive with AWS Backup is only available for snapshots with a backup frequency of one month or longer (28-day cron expression) and a retention of more than 90 days. This is a protective measure to ensure that you don’t archive snapshots, such as hourly snapshots that wouldn’t benefit from the transition to the cold tier.

Backup frequency

The ability to archive Amazon EBS Snapshots is a new parameter of the Lifecycle section of the AWS Backup Plans. You must explicitly opt into moving your Amazon EBS Snapshots to cold storage, because this has different properties of our existing cold storage including:

  1. Always converting an incremental backup to a full backup.
  2. Longer recovery time objective (RTO) (up to 72 hours).
  3. Limitations on the frequency of backups that can be transitioned to cold storage (monthly or greater).

Time in warm storage indicates how long the backups will remain in warm storage before they are transitioned to cold storage. Total retention period is the total time the backups will be retained by AWS Backup, and its value is the sum of both warm and cold storage. For backups in cold storage, the minimum retention period is 90 days. This is why the default total retention is 98 days (8 days in warm + 90 days in cold). The bar graph shows the total retention of your backups and where the backups will reside during that time. In the example shown in this graph, 8 days is in warm storage (red bar), and 90 days is in cold storage (blue bar).

Cold storage for Amazon EBS Snapshots

To restore or use the archived Amazon EBS snapshot today (outside of AWS Backup), you have to follow a two-step process:

  1. Temporarily or permanently restore the snapshot from archive to standard tier.
  2. Once it’s in standard tier, call the CreateVolume API from the standard tier.

With this announcement, using either the AWS Backup console or the API to restore the archived Amazon EBS snapshot in AWS Backup, the following restore workflow applies:

  1. Enter the number of days you want to temporarily restore your snapshot from cold to standard tier.
  2. Choose your volume configuration.

Restore archived EBS snapshot

The end result will be a restored EBS volume. You will not have to manually move the snapshot from cold to standard tier, then restore the volume, this will be done automatically for you.

Now available
Amazon EBS Snapshots Archive with AWS Backup is available for you today in all AWS Regions except China and AWS GovCloud (US).

As usual, you pay as you go, with no minimum or fixed fees. There are two metrics that influence Amazon EBS Snapshots Archive billing: data storage and data retrieval. You are charged for a 90-day period at minimum. This means that if you delete a snapshot archive or permanently restore it less than 90 days after creation, then we charge for the full 90-day period. The AWS Backup pricing page has the details.


Automatic restore testing and validation now available in AWS Backup

Post Syndicated from Veliswa Boya original

Performing automatic game day testing of all your critical resources is an important step in determining that you are prepared to respond to ransomware or any data loss event. This gives you the opportunity to take appropriate corrective actions based on the results and monitor results such as success or failure from these tests. Ultimately, you will be able to ascertain if the restore times meet your expected organization’s recovery time objective (RTO) goals, helping you develop improved recovery strategies.

Today, we’re announcing restore testing, a new capability in AWS Backup that allows you to perform restore testing of your AWS resources across storage, compute, and databases. With this feature, you can automate the entire restore testing process and avoid surprises later by determining now whether you can successfully recover using your backups in the event of a data loss such as ransomware. As an additional option, to demonstrate compliance with your organizational and regulatory data governance requirements, you can use the restore job results.

How it works
Restore testing in AWS Backup supports restore testing of resources for which the recovery points are created by AWS Backup, and the following services are supported: Amazon Elastic Block Store (Amazon EBS), Amazon Elastic Compute Cloud (Amazon EC2), Amazon Aurora, Amazon Relational Database Service (Amazon RDS), Amazon Elastic File Store (Amazon EFS), Amazon Simple Storage Service (Amazon S3), Amazon DynamoDB, Amazon FSx, Amazon DocumentDB, and Amazon Neptune. You can get started with restore testing from the AWS Backup console, AWS CLI, or AWS SDK.

Earlier, I created EC2 instances and a backup of these instances. Then, I created my restore testing plan in the AWS Backup console.

Create restore testing plan

In this General section, I enter the name of the plan, a test frequency, a Start time, and a Start within. Start time sets the time for the test to begin, for example, if you have a daily test frequency set, you specify what time the plan will run each day. Start within is the period of time in which the restore test is designated to begin. AWS Backup makes a best effort to commence all designated restore jobs during the Start within time window. You have a choice to keep this very minimal or very large based on your preference.

Figure 2: Section 1 Create restore testing plan

In the Recovery point selection section, I specify the vaults that the recovery points should come from, and a timeframe of eligible recovery points as part of this restore testing plan. I left the criteria for a recovery point at the default selection. I also didn’t opt to include recovery points generated by point-in-time recovery (PITR) in this restore testing plan.


Tagging is optional so for the purposes of this test I didn’t add a tag. I was then finished with setup, and it was time for me to choose Create restore testing plan to proceed with creating this restore testing plan.

Figure 4: Finalize creation of restore testing plan

Once the restore testing plan has been created, it is time to assign resources. I start by specifying the IAM role that AWS Backup will assume when running the restore test. In terms of retention period before cleanup, I kept the default selection of deleting the restored resources immediately, to optimize costs. Alternatively, by specifying a retention period I could have also configured to integrate my own tests (for example, AWS Lambda) using Amazon EventBridge (CloudWatch Events) and send back validation status using the new PutRestoreValidationResult API so that it is reported in the restore job.


I have EC2 instances that I created and backed up earlier, and I specify that this plan is for Amazon EC2 resource types. I include all protected resources of this EC2 resource type in the selection scope. I have very few resources, so I didn’t add the optional tags.


I opted to use the default instance type for the restore. I also didn’t specify any additional parameters. It’s then time to choose Assign resources.


Once the resources have been assigned, all information related to the restore testing plan will be presented in a summarized form where you’ll be able to see when the restore testing jobs have executed.

Once I have enough restores performed over time, I can also view the Restore time history for every resource restored from the Protected resources tab.

Now available
Restore testing in AWS Backup is available in all AWS Regions where AWS Backup is available except AWS China Regions, AWS GovCloud (US), and Israel (Tel Aviv).
To learn more, visit the AWS Backup user guide. You can submit your questions to AWS re:Post for AWS Backup or through your usual AWS Support contacts.

— Veliswa

Amazon Transcribe Call Analytics adds new generative AI-powered call summaries (preview)

Post Syndicated from Veliswa Boya original

We are announcing generative artificial intelligence (AI)-powered call summarization in Amazon Transcribe Call Analytics in preview. Powered by Amazon Bedrock, this feature helps businesses improve customer experience, and agent and supervisor productivity by automatically summarizing customer service calls. Amazon Transcribe Call Analytics provides machine learning (ML)-powered analytics that allows contact centers to understand the sentiment, trends, and policy compliance of customer conversations to improve their experience and identify crucial feedback. A single API call is all it takes to extract transcripts, rich insights, and summaries from your customer conversations.

We understand that as a business, you want to maintain an accurate historical record of key conversation points, including action items associated with each conversation. To do this, agents summarize notes after the conversation has ended and enter these in their CRM system, a process that is time-consuming and subject to human error. Now imagine the customer trust erosion that follows when the agent fails to correctly capture and act upon important action items discussed during conversations.

How it works
Starting today, to assist agents and supervisors with the summarization of customer conversations, Amazon Transcribe Call Analytics will generate a concise summary of a contact center interaction that captures key components such as why the customer called, how the issue was addressed, and what follow-up actions were identified. After completing a customer interaction, agents can directly proceed to help the next customer since they don’t have to summarize a conversation, resulting in reduced customer wait times and improved agent productivity. Further, supervisors can review the summary when investigating a customer issue to get a gist of the conversation, without having to listen to the entire call recording or read the transcript.

Exploring Amazon Transcribe Call Analytics in the console
To see how this works visually, I first create an Amazon Simple Storage Service (Amazon S3) bucket in the relevant AWS Region. I then upload the audio file to the S3 bucket.

Audio file in S3 bucket

To create an analytics job that transcribes the audio and provides additional analytics about the conversation that the customer and the agent were having, I go to the Amazon Transcribe Call Analytics console. I select Post-call Analytics in the left hand navigation bar and then choose Create job.

Create Post-call analytics job

Next I enter a job name making sure to keep the language settings based on the language in the audio file.

Job settings

In the Amazon S3 URI path, I provide the link to the audio file uploaded in the first screenshot shown in this post.

Audio file details

In Role name, I select Create an IAM role which will have access to the Amazon S3 bucket, then choose Next.

Create IAM Role

I enable Generative call summarization, and then choose Create job.

Configure job

After a few minutes, the job’s status will change from In progress to Complete, indicating that it was completed successfully.

Job status

Select the job, and the next screen will show the transcript and a new tab, Generative call summarization – preview.

You can also download the transcript to view the analytics and summary.

Now available
Generative call summarization in Amazon Transcribe Call Analytics is available today in English in US East (N. Virginia) and US West (Oregon).

With generative call summarization in Amazon Transcribe Call Analytics, you pay as you go and are billed monthly based on tiered pricing. For more information, see Amazon Transcribe pricing.

Learn more:


New – AWS Audit Manager now supports first third-party GRC integration

Post Syndicated from Veliswa Boya original

Auditing is a continuous and ongoing process, and every audit includes the collection of evidence. The evidence gathered helps confirm the state of resources and it’s used to demonstrate that the customer’s policies, procedures, and activities (controls), are in place, and that the control has been operational for a specified period of time. AWS Audit Manager already automates this evidence collection for AWS usage. However, large enterprise organizations who deploy their workloads across a range of locations such as cloud, on-premises, or a combination of both, manage this evidence data using a combination of third-party or homegrown tools, spreadsheets, and emails.

Today we’re excited to announce the integration of AWS Audit Manager with third party Governance, Risk, and Compliance (GRC) provider, MetricStream CyberGRC, an AWS Partner with GRC capabilities. This integration allows enterprises to manage compliance across AWS, on-premises, and other cloud environments in a centralized GRC environment.

Before this announcement, Audit Manager operated only in the AWS context, allowing customers to collect compliance evidence for resources in AWS. They would then relay that information to their GRC systems external to AWS for additional aggregation and analysis. This process left customers without an automated way to monitor and evaluate all compliance data in one centralized location, resulting in delays to compliance outcomes.

The GRC integration with Audit Manager allows you to use audit evidence collected by Audit Manager directly in MetricStream CyberGRC. Audit Manager now receives the controls in scope from MetricStream CyberGRC, collects evidence around these controls, and exports the data related to the audit into MetricStream CyberGRC for aggregation and analysis. You will now have aggregated compliance, real-time monitoring and centralized reporting. This will reduce compliance fatigue and improve stakeholder collaboration.

How It Works
Using Amazon Cognito User Pools, you’ll be onboarded into the multi-tenant instance of MetricStream CyberGRC.

Amazon Cognito User Pools diagram

Amazon Cognito User Pools

Once onboarded, you’ll be able to view AWS assets and frameworks inside MetricStream CyberGRC. You can then begin by choosing the suitable Audit Manager framework to define the relationships between your existing enterprise controls and AWS controls. After creating this one-time control mapping, you can define the accounts in scope to create an assessment that MetricStream CyberGRC will manage in AWS Audit Manager on your behalf. This assessment triggers AWS Audit Manager to collect evidence in context of the mapped controls. As a result, you get a unified view of compliance evidence inside your GRC application. Any standard controls that you have in Audit Manager will be provided to MetricStream CyberGRC by using the GetControl API to facilitate manual mapping process wherever automated mapping fails or does not suffice. The EvidenceFinder API will send bulk evidence from Audit Manager to MetricStream CyberGRC.

Available Now
This feature is available today where Audit Manager (AWS Regions) and MetricStream CyberGRC are both available. There are no additional AWS Audit Manager charges for using this integration. To use this integration, please reach out to MetricStream for information about access and purchase of MetricStream CyberGRC software.

As part of the AWS Free Tier, AWS Audit Manager offers a free tier for first-time customers. The free tier will expire in two calendar months after the first subscription. For more information, see AWS Audit Manager pricing. To learn more about AWS Audit Manager integration with MetricStream CyberGRC, see Audit Manager documentation.


New – Manage Planned Lifecycle Events on AWS Health

Post Syndicated from Veliswa Boya original

We are announcing new features in AWS Health to help you manage planned lifecycle events for your AWS resources and dynamically track the completion of actions that your team takes at the resource-level to ensure continued smooth operations of your applications. Some examples of planned lifecycle events are an Amazon Elastic Kubernetes Service (Amazon EKS) Kubernetes version end of standard support, Amazon Relational Database Service (Amazon RDS) certificate rotations, and end of support for other open source software, to name a few.

These features include:

  • The ability to dynamically track the completion of actions at the resource level where possible, to minimize disruption to applications.
  • Timely visibility into upcoming planned lifecycle events, using notifications at least 90 days in advance for minor changes, and 180 days in advance for major changes, whenever possible.
  • A standardized data format that helps you prepare and take actions. It integrates AWS Health events programmatically with your preferred operational tools, using AWS Health API.
  • An organization-wide visibility into planned lifecycle events for teams that manage workloads across the company with delegated administrator. This means that central teams such as Cloud Center of Excellence (CCoE) teams, no longer need to use the management account to access the organizational view.
  • A single feed of AWS Health events from all accounts in your organization on Amazon EventBridge. This provides a centralized way to automate the management of AWS Health events across your organization by creating rules on EventBridge to take actions. Depending on the type of event, you can capture event information, initiate additional events, send notifications, take corrective action, or perform other actions. For example, you can use AWS Health to receive email, AWS Chatbot, or push notifications to the AWS Console Mobile Application if you have AWS resources in your AWS account that are scheduled for updates, such as Amazon Elastic Compute Cloud (Amazon EC2) instances.

How it Works
Planned lifecycle events are available through the AWS Health Dashboard, AWS Health API, and EventBridge. You can automate the management of AWS Health events across your organization by creating rules on EventBridge that includes the “source”: [“”] value to receive AWS Health notifications or initiate actions based on the rules created. For example, if AWS Health publishes an event about your EC2 instances, then you can use these notifications to take action and update or replace your resources as needed. You can view the planned lifecycle events for your AWS resources in the Scheduled changes tab.

Table View - Organizational Level

Table View – Organizational level

To prioritize events, you can now see scheduled changes in a calendar view. The event has a start time to indicate when the change commences. The status remains as Upcoming until the change occurs or all of the affected resources have been actioned. The event status changes to Completed when all of the affected resources have been actioned. You can also deselect event statuses that you don’t want to focus on. To show more specific event details, select an event to open the split panel view to the right or the bottom of the screen.

Calendar event selected - Organizational level (Affected resources)

Calendar event selected – Organizational level (Affected resources)

When selecting the Affected resources tab on the detailed view of an event, customers can see relevant account information that can help you reach out to the right people to resolve impaired resources.

Affected resources view - Account level

Affected resources view – Account level

Integration with Other AWS Services
Using EventBridge integrations that already exist in AWS Health, you can send change events, and their fully managed lifecycles to other tools such as JIRA, ServiceNow, and AWS Systems Manager OpsCenter. EventBridge sends all updates to events (for example, timestamps, resource status, and more) to these tools, allowing you to track the status of events in your preferred tooling.

EventBridge Integrations

EventBridge integrations

Now Available
Planned lifecycle events for AWS Health are available in all AWS Regions where AWS Health is available except China and GovCloud Regions.
To learn more, visit the AWS Health user guide. You can submit your questions to AWS re:Post for AWS Health, or through your usual AWS Support contacts.


AWS Weekly Roundup – Amazon Bedrock Is Now Generally Available, Attend AWS Innovate Online, and More – Oct 2, 2023

Post Syndicated from Veliswa Boya original

Last week I attended the AWS Summit Johannesburg. This was the first summit to be hosted in my own country and my own city since 2019 so it was very special to have the opportunity to attend. It was great to get to meet with so many of our customers and hear how they are building on AWS.

Now on to the AWS updates. I’ve compiled a few announcements and upcoming events you need to know about. Let’s get started!

Last Week’s Launches
Amazon Bedrock Is Now Generally Available – Amazon Bedrock was announced in preview in April of this year as part of a set of new tools for building with generative AI on AWS. Last week’s announcement of this service being generally available was received with a lot of excitement and customers have already been sharing what they are building with Amazon Bedrock. I quite enjoyed this lighthearted post from AWS Serverless Hero Jones Zachariah Noel about the “Bengaluru with traffic-filled roads” image he produced using Stability AI’s Stable Diffusion XL image generation model on Amazon Bedrock.

Amazon MSK Introduces Managed Data Delivery from Apache Kafka to Your Data Lake – Amazon MSK was released in 2019 to help our customers reduce the work needed to set up, scale, and manage Apache Kafka in production. Now you can continuously load data from an Apache Kafka cluster to Amazon Simple Storage Service (Amazon S3).

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

The Community.AWS Blog is where builders share and learn with the community of cloud enthusiasts. Contributors to this blog include AWS employees, AWS Heroes, AWS Community Builders, and other members of the AWS Community. Last week, AWS Hero Johannes Koch published this awesome post on how to build a simple website using Flutter that interacts with a serverless backend powered by AppSync-merged APIs.

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

Upcoming AWS Events
We have the following upcoming events:

AWS Cloud Days (October 10, 24) – Connect and collaborate with other like-minded folks while learning about AWS at the AWS Cloud Day in Athens and Prague.

AWS Innovate Online (October 19)Register for AWS Innovate Online to learn how you can build, run, and scale next-generation applications on the most extensive cloud platform. There will be 80+ sessions delivered in five languages and you’ll receive a certificate of attendance to showcase all you’ve learned.

We’re focused on improving our content to provide a better customer experience, and we need your feedback to do so. Take this quick survey to share insights on your experience with the AWS Blog. Note that this survey is hosted by an external company, so the link doesn’t lead to our website. AWS handles your information as described in the AWS Privacy Notice.


New – Amazon EC2 R7iz Instances Memory-Optimized for High CPU Performance, Memory-Intensive Workloads

Post Syndicated from Veliswa Boya original

Today we’re announcing general availability of the Amazon EC2 R7iz instances. R7iz instances are the fastest 4th Generation Intel Xeon Scalable-based (Sapphire Rapids) instances in the cloud with 3.9 GHz sustained all-core turbo frequency. R7iz instances are suitable for workloads where there’s a requirement for more memory to process additional data, larger sizes of instances to scale up, higher compute and memory performance to reduce completion times, and higher networking and Amazon Elastic Block Store (Amazon EBS) performance to improve latency. The high compute performance of the R7iz instances, combined with a large amount of memory, results in increased overall performance for applications that include front-end electronic design automation (EDA), relational database workloads with high per core licensing fees, and financial, actuarial, and data analytics simulation workloads. This can help you speed time to market for product development while reducing licensing costs.

R7iz Instances

The specs for the R7iz instances are as follows.

Memory (GiB)
Network Bandwidth
EBS Bandwidth
r7iz.large 2 16 Up to 12.5 Gbps Up to 10 Gbps
r7iz.xlarge 4 32 Up to 12.5 Gbps Up to 10 Gbps
r7iz.2xlarge 8 64 Up to 12.5 Gbps Up to 10 Gbps
r7iz.4xlarge 16 128 Up to 12.5 Gbps Up to 10 Gbps
r7iz.8xlarge 32 256 12.5 Gbps 10 Gbps
r7iz.12xlarge 48 384 25 Gbps 19 Gbps
r7iz.16xlarge 64 512 25 Gbps 20 Gbps
r7iz.32xlarge 128 1024 50 Gbps 40 Gbps

You can attach up to 88 EBS volumes to each R7iz instance; by way of comparison, the z1d instances allow you to attach up to 28 volumes.

We are also getting ready to launch two sizes of bare metal R7iz instances:

Memory (GiB)
Network Bandwidth
EBS Bandwidth
r7iz.metal-16xl 64 512 25 Gbps 20 Gbps
r7iz.metal-32xl 128 1024 50 Gbps 40 Gbps

 Built-in Accelerators
R7iz instances also include four built-in accelerators: Advanced Matrix Extensions (AMX), Intel Data Streaming accelerator (DSA), Intel In-Memory Analytics Accelerator (IAA), and Intel QuickAssist Technology( QAT). Some of these accelerators require the use of specific kernel versions, drivers, and/or compilers. The Advanced Matrix Extensions are available on all sizes of R7iz instances while the Intel QAT, Intel IAA, and Intel DSA accelerators will be available on the r7iz.metal-16xl and r7iz.metal-32xl instances (coming soon).

Available Now
R7iz instances are generally available today in the US East (N. Virginia), and US West (Oregon) AWS Regions. As usual with Amazon EC2, you pay only for what you use. For more information, see Amazon EC2 pricing.

To learn more, visit our Amazon EC2 R7iz instances page, and please send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.


New — File Release for Amazon FSx for Lustre

Post Syndicated from Veliswa Boya original

Amazon FSx for Lustre provides fully managed shared storage with the scalability and high performance of the open-source Lustre file systems to support your Linux-based workloads. FSx for Lustre is for workloads where storage speed and throughput matter. This is because FSx for Lustre helps you avoid storage bottlenecks, increase utilization of compute resources, and decrease time to value for workloads that include artificial intelligence (AI) and machine learning (ML), high performance computing (HPC), financial modeling, and media processing. FSx for Lustre integrates natively with Amazon Simple Storage Service (Amazon S3), synchronizing changes in both directions with automatic import and export, so that you can access your Amazon S3 data lakes through a high-performance POSIX-compliant file system on demand.

Today, I’m excited to announce file release for FSx for Lustre. This feature helps you manage your data lifecycle by releasing file data that has been synchronized with Amazon S3. File release frees up storage space so that you can continue writing new data to the file system while retaining on-demand access to released files through the FSx for Lustre lazy loading from Amazon S3. You specify a directory to release from, and optionally a minimum amount of time since last access, so that only data from the specified directory, and the minimum amount of time since last access (if specified), is released. File release helps you with data lifecycle management by moving colder file data to S3 enabling you to take advantage of S3 tiering.

File release tasks are initiated using the AWS Management Console, or by making an API call using the AWS CLI, AWS SDK, or Amazon EventBridge Scheduler to schedule release tasks at regular intervals. You can choose to receive completion reports at the end of your release task if so desired.

Initiating a Release Task
As an example, let’s look at how to use the console to initiate a release task. To specify criteria for files to release (for example, directories or time since last access), we define release data repository tasks (DRTs). DRTs release all files that are synchronized with Amazon S3 and that meet the specified criteria. It’s worth noting that release DRTs are processed in sequence. This means that if you submit a release DRT while another DRT (for example, import or export) is in progress, the release DRT will be queued but not processed until after the import or export DRT has completed.

Note: For the data repository association to work, automatic backups for the file system must be disabled (use the Backups tab to do this). Secondly, ensure that the file system and the associated S3 bucket are in the same AWS Region.

I already have an FSx for Lustre file system my-fsx-test.

I create a data repository association, which is a link between a directory on the file system and an S3 bucket or prefix.

I specify the name of the S3 bucket or an S3 prefix to be associated with the file system.

After the data repository association has been created, I select Create release task.

The release task will release directories or files that you want to release based on your specific criteria (again, important to remember that these files or directories must be synchronized with an S3 bucket in order for the release to work). If you specified the minimum last access for release (in addition to the directory), files that have not been accessed more recently than that will be released.

In my example, I chose to Disable completion reports. However, if you choose to Enable completion reports, the release task will produce a report at the end of the release task.

Files that have been released can still be accessed using existing FSx for Lustre functionality to automatically retrieve data from Amazon S3 back to the file system on demand. This is because, although released, their metadata stays on the file system.

File release won’t automatically prevent your file system from becoming full. It remains important to ensure that you don’t write more data than the available storage capacity before you run the next release task.

Now Available
File release on FSx for Lustre is available today in all AWS Regions where FSx for Lustre is supported, on all new or existing S3-linked file systems running Lustre version 2.12 or later. With file release on FSx for Lustre, there is no additional cost. However, if you release files that you later access again from the file system, you will incur normal Amazon S3 request and data retrieval costs where applicable when those files are read back into the file system.

To learn more, visit the Amazon FSx for Lustre Page, and please send feedback to AWS re:Post for Amazon FSx for Lustre or through your usual AWS support contacts.


Welcome to AWS Storage Day 2023

Post Syndicated from Veliswa Boya original

Welcome to the fifth annual AWS Storage Day! This virtual event is happening today starting at 9:00 AM Pacific Time (12:00 PM Eastern Time) and is available for you to watch on the AWS On Air Twitch channel. The first AWS Storage Day was hosted in 2019, and this event has grown into an innovation day that we look forward to delivering to you every year. In last year’s Storage Day post, I wrote about the constant innovations in AWS Storage aimed at helping you put your data to work while keeping it secure and protected. This year, Storage Day is focused on storage for AI/ML, data protection and resiliency, and the benefits of moving to the cloud.

AWS Storage Day Key Themes
When it comes to storage for AI/ML, data volumes are increasing at an unprecedented rate, exploding from terabytes to petabytes and even to exabytes. With a modern data architecture on AWS, you can rapidly build scalable data lakes, use a broad and deep collection of purpose-built data services, scale your systems at a low cost without compromising performance, share data across organizational boundaries, and manage compliance, security, and governance, allowing you to make decisions with speed and agility at scale.
To train machine learning models and build Generative AI applications, you must have the right data strategy in place. So, I’m happy to see that, among the list of sessions to look forward to at the live event, the Optimize generative AI and ML with AWS Infrastructure session will discuss how you can transform your data into meaningful insights.

Whether you’re just getting started with the cloud, planning to migrate applications to AWS, or already building applications on AWS, we have resources to help you protect your data and meet your business continuity objectives. Our data protection and resiliency features and solutions can help you meet your business continuity goals and deliver disaster recovery during data loss events, across recovery point and time objectives (RPO and RTO). With the unprecedented data growth happening in the world today, determining where your data is stored, how it’s secured, and who has access to it is a higher priority than ever. Be sure to join the Protect data in AWS amid a rapidly evolving cyber landscape session to learn more.

When moving data to the cloud, you need to understand where you’re moving it for different use cases, the types of data you’re moving, and the network resources available, among other considerations. There are many reasons to move to the cloud, recently, Enterprise Strategy Group (ESG) validated that organizations reduced compute, networking, and storage costs by up to 66 percent by migrating on-premises workloads to AWS Cloud infrastructure. ESG confirmed that migrating on-premises workloads to AWS provides organizations with reduced costs, increased performance, improved operational efficiency, faster time to value, and improved business agility.
We have a number of sessions that discuss how to move to the cloud, based on your use case. I’m most looking forward to the Hybrid cloud storage and edge compute: AWS, where you need it session, which will discuss considerations for workloads that can’t fully move to the cloud.

Tune in to learn from experts about new announcements, leadership insights, and educational content related to the broad portfolio of AWS Storage services and features that address all these themes and more. Today, we have announcements related to Amazon Simple Storage Service (Amazon S3), Amazon FSx for Windows File Server, Amazon Elastic File System (Amazon EFS), Amazon FSx for OpenZFS, and more.

Let’s get into it.

15 Years of Amazon EBS
Not long ago, I was reading Jeff Barr’s post titled 15 Years of AWS Blogging! In this post, Jeff mentioned a few posts he wrote for the earliest AWS services and features. Amazon Elastic Block Store (Amazon EBS) is on this list as a service that simplifies the use of Amazon EC2.

Well, it’s been 15 years since the launch of Amazon EBS was announced, and today we celebrate 15 years of this service. If you were one of the original users who put Amazon EBS to good use and provided us with the very helpful feedback that helped us invent and simplify, iterate and improve, I’m sure you can’t believe how time flies. Today, Amazon EBS handles more than 100 trillion I/O operations daily, and over 390 million EBS volumes are created every day.

If you’re new to Amazon EBS, join us for a fireside chat with Matt Garman, Senior Vice President, Sales, Marketing, and Global Services at AWS, and learn the strategy and customer challenges behind the launch of the service in 2008. You’ll also hear from long-term EBS customer, Stripe, about its growth with EBS since Stripe was launched 12 years ago.

Amazon EBS has continuously improved its scalability and performance to support more customer workloads as the direct storage attachment for Amazon EC2 instances. With the launch of Amazon EC2 M7i instances, powered by custom 4th Generation Intel Xeon Scalable processors, on August 2, you can attach up to 128 Amazon EBS volumes, an increase from 28 on a previous generation M6i instance. The higher number of volume attachments means you can increase storage density per instance and improve resource utilization, reducing total compute cost.

You can host up to 127 containers per instance for larger database applications and scale them more cost effectively before needing to provision more instances and only pay for resources you need. With a higher number of volume attachments, you can fully utilize the memory and vCPU available on these powerful M7i instances as your database storage footprint grows. EBS is also increasing the number of multi-volume snapshots you can create, for up to 128 EBS volumes attached to an instance, enabling you to create crash-consistent backups of all volumes attached to an instance.

Join the 15 years of innovations with Amazon EBS session for a discussion about how the original vision for Amazon EBS has evolved to meet your growing demands for cloud infrastructure.

Mountpoint for Amazon S3
Now generally available, Mountpoint for Amazon S3 is a new open source file client that delivers high throughput access, lowering compute costs for data lakes on Amazon S3. Mountpoint for Amazon S3 is a file client that translates local file system API calls to S3 object API calls. Using Mountpoint for Amazon S3, you can mount an Amazon S3 bucket as a local file system on your compute instance, to access your objects through a file interface with the elastic storage and throughput of Amazon S3. Mountpoint for Amazon S3 supports sequential and random read operations on existing files, and sequential write operations for creating new files.

The Deep dive and demo of Mountpoint for Amazon S3 session demonstrates how to use the file client to access objects in Amazon S3 using file APIs, making it easier to store data at scale and maximize the value of your data with analytics and machine learning workloads. Read this blog post to learn more about Mountpoint for Amazon S3 and how to get started, including a demo.

Put Cold Storage to Work Faster with Amazon S3 Glacier Flexible Retrieval
Amazon S3 Glacier Flexible Retrieval improves data restore time by up to 85 percent, at no additional cost. Faster data restores automatically apply to the Standard retrieval tier when using Amazon S3 Batch Operations. These restores begin to return objects within minutes, so you can process restored data faster. Processing restored data in parallel with ongoing restores helps you accelerate data workflows and quickly respond to business needs. Now, whether you’re transcoding media, restoring operational backups, training machine learning models, or analyzing historical data, you can speed up your data restores from archive.

Coupled with the S3 Glacier improvements to restore throughput by up to 10 times for millions of objects announced in 2022, S3 Glacier data restores of all sizes now benefit from both faster starts and shorter completion times.

Join the Maximize the value of cold data with Amazon S3 Glacier session to learn how Amazon S3 Glacier is helping organizations of all sizes and from all industries transform their data archiving to unlock business value, increase agility, and save on storage costs. Read this blog post to learn more about the Amazon S3 Glacier Flexible Retrieval performance improvements and follow step-by-step guidance on how to get started with faster standard retrievals from S3 Glacier Flexible Retrieval.

Supporting a Broad Spectrum of File Workloads
To serve a broad spectrum of use cases that rely on file systems, we offer a portfolio of file system services, each targeting a different set of needs. Amazon EFS is a serverless file system built to deliver an elastic experience for sharing data across compute resources. Amazon FSx makes it easier and cost-effective for you to launch, run, and scale feature-rich, high-performance file systems in the cloud, enabling you to move to the cloud with no changes to your code, processes, or how you manage your data.

Power ML research and big data analytics with Amazon EFS
Amazon EFS offers serverless and fully scalable file storage, designed for high scalability in both storage capacity and throughput performance. Just last week, we announced enhanced support for faster read and write IOPS, making it easier to power more demanding workloads. We’ve improved the performance capabilities of Amazon EFS by adding support for up to 55,000 read IOPS and up to 25,000 write IOPS per file system. These performance enhancements help you to run more demanding workflows, such as machine learning (ML) research with KubeFlow, financial simulations with IBM Symphony, and big data processing with Domino Data Lab, Hadoop, and Spark.

Join the Build and run analytics and SaaS applications at scale session to hear how recent Amazon EFS performance improvements can help power more workloads.

Multi-AZ file systems on Amazon FSx for OpenZFS
You can now use a multi-AZ deployment option when creating file systems on Amazon FSx for OpenZFS, making it easier to deploy file storage that spans multiple AWS Availability Zones to provide multi-AZ resilience for business-critical workloads. With this launch, you can take advantage of the power, agility, and simplicity of Amazon FSx for OpenZFS for a broader set of workloads, including business-critical workloads like database, line-of-business, and web-serving applications that require highly available shared storage that spans multiple AZs.

The new multi-AZ file systems are designed to deliver high levels of performance to serve a broad variety of workloads, including performance-intensive workloads such as financial services analytics, media and entertainment workflows, semiconductor chip design, and game development and streaming, up to 21 GB per second of throughput and over 1 million IOPS for frequently accessed cached data, and up to 10 GB per second and 350,000 IOPS for data accessed from persistent disk storage.

Join the Migrate NAS to AWS to reduce TCO and gain agility session to learn more about multi-AZs with Amazon FSx for OpenZFS.

New, Higher Throughput Capacity Levels on Amazon FSx for Windows File Server
Performance improvements for Amazon FSx for Windows File Server help you accelerate time-to-results for performance-intensive workloads such as SQL Server databases, media processing, cloud video editing, and virtual desktop infrastructure (VDI).

We’re adding four new, higher throughput capacity levels to increase the maximum I/O available up to 12 GB per second from the previous I/O of 2 GB per second. These throughput improvements come with correspondingly higher levels of disk IOPS, designed to deliver an increase up to 350,000 IOPS.

In addition, by using FSx for Windows File Server, you can provision IOPS higher than the default 3 IOPS per GiB for your SSD file system. This allows you to scale SSD IOPS independently from storage capacity, allowing you to optimize costs for performance-sensitive workloads.

Join the Migrate NAS to AWS to reduce TCO and gain agility session to learn more about the performance improvements for Amazon FSx for Windows File Server.

Logically Air-Gapped Vault for AWS Backup
AWS Backup is a fully managed, policy-based data protection solution that enables customers to centralize and automate backup restores across 19 AWS services (spanning compute, storage, and databases) and third-party applications such as VMware Cloud on AWS and on-premises, as well as SAP HANA on Amazon EC2.

Today, we’re announcing the preview of logically air-gapped vault as a new type of AWS Backup Vault that acts as an additional layer of protection to mitigate against malware events. With logically air-gapped vault, customers can recover their application data through a different trusted account.

Join the Deep dive on data recovery for ransomware events session to learn more about logically air-gapped vault for AWS Backup.

Copy Data to and from Other Clouds with AWS DataSync
AWS DataSync is an online data movement and discovery service that simplifies data migration and helps you quickly, easily, and securely transfer your file or object data to, from, and between AWS storage services. In addition to support of data migration to and from AWS storage services, DataSync supports copying to and from other clouds such as Google Cloud Storage, Azure Files, and Azure Blob Storage. Using DataSync, you can move your object data at scale between Amazon S3 compatible storage on other clouds and AWS storage services such as Amazon S3. We’re now expanding the support of DataSync for copying data to and from other clouds to include DigitalOcean Spaces, Wasabi Cloud Storage, Backblaze B2 Cloud Storage, Cloudflare R2 Storage, and Oracle Cloud Storage.

Join the Identify and accelerate data migrations at scale session to learn more about this expanded support for DataSync.

Join Us Online
Join us today for the AWS Storage Day virtual event on the AWS On Air channel on Twitch. The event will be live starting at 9:00 AM Pacific Time (12:00 PM Eastern Time) on August 9. All sessions will be available on demand approximately two days after Storage Day.

We look forward to seeing you on Twitch!

– Veliswa 

AWS Weekly Roundup – AWS Storage Day, AWS Israel (Tel Aviv) Region, and More – Aug 8, 2023

Post Syndicated from Veliswa Boya original

(Editor’s note: Today, we are changing the title of this regular weekly post from AWS Week in Review to AWS Weekly Roundup to better reflect the mix of recent top news and announcements as well as upcoming events you won’t want to miss.)

It’s taken me some time to finally be comfortable with being in front of a camera, a strange thing for a Developer Advocate to say I know! Last week I joined a couple of my team-mates at the AWS London Studios to record a series of videos that will be published in our Build On AWS YouTube Channel. Build On AWS is for the hands-on, technical AWS cloud builder who wants to become more agile and innovate faster. In the channel, you’ll find dynamic, high-quality content that’s designed for developers, by developers!

This video tells you more about what you’ll find in the channel. Check it out and consider subscribing to not miss out when we publish new content.

Now on to the AWS updates. There was a lot of news related to AWS last week, and I’ve compiled a few announcements and upcoming events you need to know about. Let’s get started!

Last Week’s Launches
Here are a few launches from last week that you might have missed:

Microsoft 365 Apps for enterprise now available on Amazon WorkSpaces servicesAmazon WorkSpaces is a fully managed, secure, and reliable virtual desktop in the AWS Cloud. With Amazon WorkSpaces, you improve IT agility and maximize user experience, while only paying for the infrastructure that you use. We announced the availability of Microsoft 365 Apps for enterprise on Amazon WorkSpaces. You can bring your own Microsoft 365 licenses (if they meet Microsoft’s licensing requirements) and activate the applications at no additional cost to run Microsoft 365 Apps for enterprise on Amazon WorkSpaces services.

AWS Israel (Tel Aviv) Region is Now Open – You can now securely store data in Israel while serving users in the vicinity with even lower latency. This is because last week we launched the Tel Aviv Region to give customers an additional option for running applications and serving users from data centers located in Israel.

Amazon Connect Launches – This is one of my favorite AWS services to write about because of how Amazon Connect is changing our customers’ engagement with their own customers. Last week, Amazon Connect announced automatic activity scheduling based on shift duration, custom flow block titles, and archiving and deleting flows from the UI, to name a few.

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

Customizable thresholds for health events supported on Amazon CloudWatch Internet Monitor – Until this announcement, the default threshold for overall availability and performance scores to invoke a health event was 95 percent. Now, you can customize the thresholds for when to invoke a health event for internet-facing traffic between your end users and your applications hosted on AWS.

Improved AWS Backup performance for Amazon S3 buckets – Now you can speed up your initial Amazon S3 backup workflow and back up buckets with more than 3 billion objects due to improvements to the speed of backups by up to 10x for buckets with more than 300 million objects. This performance improvement is automatically enabled at no additional cost in all Regions where AWS Backup support for Amazon S3 is available.

For AWS open-source news and updates, check out the latest newsletter curated by my colleague Ricardo Sueiras to bring you the most recent updates on open-source projects, posts, events, and more.

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

Upcoming AWS Events
We have the following upcoming events:

AWS Storage Day (August 9) – A one-day virtual event where you’ll learn how to prepare for AI/ML with the storage decisions you make now, how to do more with your budget by optimizing storage costs for on-premises and cloud data, and how to deliver holistic data protection for your organization, including recovery planning to help protect against ransomware. Learn more and register here.

AWS Summit Mexico City (August 30)Sign up for the Summit to connect and collaborate with other like-minded folks while learning about AWS.

AWS Community Days (August 12, 19) – Join these community-led conferences where event logistics and content are planned, sourced, and delivered by community leaders: Colombia (August 12), and West Africa (August 19).


P.S. We’re focused on improving our content to provide a better customer experience, and we need your feedback to do so. Take this quick survey to share insights on your experience with the AWS Blog. Note that this survey is hosted by an external company, so the link doesn’t lead to our website. AWS handles your information as described in the AWS Privacy Notice.

– Veliswa

AWS Week in Review – Amazon Security Lake Now GA, New Actions on AWS Fault Injection Simulator, and More – June 5, 2023

Post Syndicated from Veliswa Boya original

Last Wednesday, I traveled to Cape Town to speak at the .Net Developer User Group. My colleague Francois Bouteruche also gave a talk but joined virtually. I enjoyed my time there—what an amazing community! Join the group in order to learn about upcoming events.

Now onto the AWS updates from last week. There was a lot of news related to AWS, and I have compiled a few announcements you need to know. Let’s get started!

Last Week’s Launches
Here are a few launches from last week that you might have missed:

Amazon Security Lake is now Generally Available – This service automatically centralizes security data from AWS environments, SaaS providers, on-premises environments, and cloud sources into a purpose-built data lake stored in your account, making it easier to analyze security data, gain a more comprehensive understanding of security across your entire organization, and improve the protection of your workloads, applications, and data. Read more in Channy’s post announcing the preview of Security Lake.

New AWS Direct Connect Location in Santiago, Chile – The AWS Direct Connect service lets you create a dedicated network connection to AWS. With this service, you can build hybrid networks by linking your AWS and on-premises networks to build applications that span environments without compromising performance. Last week we announced the opening of a new AWS Direct Connect location in Santiago, Chile. This new Santiago location offers dedicated 1 Gbps and 10 Gbps connections, with MACsec encryption available for 10 Gbps. For more information on over 115 Direct Connect locations worldwide, visit the locations section of the Direct Connect product detail pages.

New actions on AWS Fault Injection Simulator for Amazon EKS and Amazon ECS – Had it not been for Adrian Hornsby’s LinkedIn post I would have missed this announcement. We announced the expanded support of AWS Fault Injection Simulator (FIS) for Amazon Elastic Kubernetes Service (EKS) and Amazon Elastic Container Service (ECS). This expanded support adds additional AWS FIS actions for Amazon EKS and Amazon ECS. Learn more about Amazon ECS task actions here, and Amazon EKS pod actions here.

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

Autodesk Uses Sagemaker to Improve Observability – One of our customers, Autodesk, used AWS services including Amazon Sagemaker, Amazon Kinesis, and Amazon API Gateway to build a platform that enables development and deployment of near-real time personalization experiments by modeling and responding to user behavior data. All this delivered a dynamic, personalized experience for Autodesk’s customers. Read more about the story at AWS Customer Stories.

AWS DMS Serverless – We announced AWS DMS Serverless which lets you automatically provision and scale capacity for migration and data replication. Donnie wrote about this announcement here.

For AWS open-source news and updates, check out the latest newsletter curated by my colleague Ricardo Sueiras to bring you the most recent updates on open-source projects, posts, events, and more.

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

Upcoming AWS Events
We have the following upcoming events. These give you the opportunity to meet with other tech enthusiasts and learn:

AWS Silicon Innovation Day (June 21) – A one-day virtual event that will allow you to understand AWS Silicon and how you can use AWS’s unique silicon offerings to innovate. Learn more and register here.

AWS Global Summits – Sign up for the AWS Summit closest to where you live: London (June 7), Washington, DC (June 7–8), Toronto (June 14).

AWS Community Days – Join these community-led conferences where event logistics and content are planned, sourced, and delivered by community leaders: Chicago, Illinois (June 15), and Chile (July 1).

And with that, I end my very first Week in Review post, and this was such fun to write. Come back next Monday for another Week in Review!

Veliswa x

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!

Announcing General Availability of Step-by-Step Guides for Amazon Connect Agent Workspace

Post Syndicated from Veliswa Boya original

At AWS re:Invent 2022 we announced the availability of step-by-step guides for Amazon Connect agent workspace in preview. My colleagues who collaborated to write the announcement post wrote about some of the challenges that contact centers face with training new agents to get up to speed with their agent desktop. They also mentioned that until agents become proficient, it takes them longer to address customer needs effectively, resulting in customer dissatisfaction.

Amazon Connect agent workspace was announced in 2021 and is a single, intuitive application that provides contact center agents with the tools that are required to onboard an agent quickly, resolve issues efficiently, and improve the customer experience. With Amazon Connect agent workspace, the agent is provided with all the tools on one screen. To think of the agent workspace, imagine the agent accepting a call, a chat, or a task and being given the necessary information about the customer and the case, plus real-time recommendations, all in one place without the need to switch between applications.

Step-by-step guides enable organizations to provide customizable experiences for their agents within the workspace, enabling them to deliver exceptional service from their first day on the job by surfacing relevant information and actions that the agent requires in order to resolve customer issues faster. This is because the step-by-step experience guides agents by identifying customer issues and then recommending subsequent actions, ensuring that the agent never has to guess or rely on past experience to know what comes next. This is helpful for both new and experienced agents. New agents can learn the system and get acquainted with their job and experienced agents can keep to the organization’s standard operating procedures instead of diverging in how they handle the same type of customer request.

Because of this intuitive experience, onboarding time for agents can be reduced by up to 50 percent, time to proficiency for the agent can be reduced by up to 40 percent, and contact handle time is reduced by up to 35 percent ultimately resulting in an improved and consistent customer experience.

A High-Level Overview of Step-by-Step Guides
During the announcement of step-by-step guides in preview, I was fascinated to learn that the experience was researched and developed in the context of Amazon Customer Service. However, step-by-step guides can also be generalized to apply to other types of organizations and use cases including the following:

  • Retail – You can customize guides to suit your retail organization, for example, guides for returning a purchase by a customer.
  • Financial Services – An example would be adding an authorized user to a credit card. Using guides, the agent can help the customer capture new user information and handle approvals through a single workflow that is consolidated within the guides.
  • Hospitality – A great example here would be creating a new reservation at a hotel by consolidating all the processes involved into a single workflow.
  • Embed as a Widget – With this, you can embed guides as a widget in your existing CRM or use APIs to bring guides to a custom workspace that you are already using in your organization.

The preview announcement post provides a deep dive into how to get started with step-by-step guides. It also shows how to deploy a sample guided experience and demonstrates how to customize guides to meet business needs. In this post we look at a high-level overview of what the agent, and the manager, can expect from step-by-step guides.

Agent experience
Step-by-step guides help with onboarding and ramping up of new agents and making them proficient faster by surfacing contextually relevant information and actions needed by agents. The intuitive experience of step-by-step guides provides agents with clear instructions of what they should be doing at any point in time when handling a particular customer case and supports agents in managing complex cases more accurately by automatically identifying issues.

As an example, when a customer calls, the agent workspace automatically presents the agent with the likely issue based on the customer’s history or current context (for instance, making a flight reservation). Then, the step-by-step experience guides the agent through the actions needed to resolve the issue quickly (such as booking a hotel after the flight reservation has been completed).

The following screenshot provides a visual image of how this might look.

Step-by-step Guides

In the UI, the agent is provided with a sequence of simple UI pages to let them focus on one thing at a time, whether that’s an input field or a question to ask the customer. They can go step by step, getting the right information that they need to help the customer’s issue. Along the way, the agent receives scripting that they can read to the customer upon successful completion of the process.

The agent can always escape out of this workflow if it turns out that the workspace surfaced the wrong one, and they can find other workflows by searching for the correct one. This allows them to self-serve and find the right solution in case what was predicted by the step-by-step guides based on the context of the contact wasn’t perfectly aligned to what they needed.

Manager experience
Amazon Connect already has a low-code, no-code builder known as Amazon Connect Flows. Flows provide a drag-and-drop experience for building IVRs, chatbots and routing logic for customers. To enable the same low-code, no-code configuration of step-by-step guides, managers are now provided with a new block within Flows known as the Show View block. The drag-and-drop experience of configuring step-by-step guides ensures that the manager no longer needs to have developers write code to build the custom workflows for the agent. Managers also no longer need to rely on static and difficult-to-follow instructions to use later to train agents.

Example of the Show View block within the Contact Flow editor

Example of the Show View block within the Contact Flow editor

Step-by-step guides are quickly created within the show view block with the help of five pre-configured views. Views are UI templates that can be used to customize the agent’s workspace, and each view is configurable. For example, you can use views to display contact attributes to an agent, provide forms for entering disposition codes, provide call notes, and present UI pages for walking agents through step-by-step guides.

The following example shows a view that we can use to create a guide for an agent that needs to book a round-trip flight for a customer. Booking this trip requires scheduling a flight to and from the destination, collecting traveler information, and asking about additional add-ons. With the form view, agents don’t have to recall all these specific steps; they can follow the wizard in their agent workspace. For each step, the agent is given form fields to fill in or options to choose from in order to quickly book the customer’s flight.

Example UI (View)

Example UI (View)

Step-by-step guides also help business operation teams figure out new ways to ensure that their agents are operating well and adjusting to new use cases. Step-by-step guides provide managers with insights into what agents do during a contact. During a workflow, data about what is shown to an agent, the decisions they made, the amount of time they spent on different steps, and what actions they took is captured and stored as a log record. Managers can use this data to improve their workflows and the agent and customer experiences.

In this post we discussed what step-by-step guides offer the agent and the manager of a contact center. Our customers are excited about how the guided experience consolidates actions into workflows and reduces the number of screens for their agents – at times from five screens down to one. In addition to all the benefits we’ve discussed in this post, guides provide you with opportunities to save between 15 – 20 percent on maintenance cost.

Now Available
Step-by-step guides are now generally available in all Regions where Amazon Connect is available, except AWS GovCloud (US-West) and Africa (Cape Town).

To learn more, refer to the Getting started with step-by-step guides for the Amazon Connect agent workspace post, and please send feedback to AWS re:Post for Amazon Connect or through your usual AWS support contacts.

Veliswa x.

Announcing Amazon DocumentDB Elastic Clusters

Post Syndicated from Veliswa Boya original

Amazon DocumentDB (with MongoDB compatibility) is a scalable, highly durable, and fully managed database service for operating mission-critical JSON workloads. It is one of AWS fast-growing services with customers including BBC, Dow Jones, and Samsung relying on Amazon DocumentDB to run their JSON workloads at scale.

Today I am excited to announce the general availability of Amazon DocumentDB Elastic Clusters. Elastic Clusters enables you to elastically scale your document database to handle virtually any number of writes and reads, with petabytes of storage capacity. Elastic Clusters simplifies how customers interact with Amazon DocumentDB by automatically managing the underlying infrastructure and removing the need to create, remove, upgrade, or scale instances.

A Few Concepts about Elastic Clusters
Sharding – A popular database concept also known as partitioning, sharding splits large data sets into smaller data sets across multiple nodes enabling customers to scale out their database beyond vertical scaling limits. Elastic Clusters uses sharding to partition data across Amazon DocumentDB’s distributed storage system. 

Elastic Clusters – Elastic Clusters is Amazon DocumentDB clusters that allow you to scale your workload’s throughput to millions of writes/reads per second and storage to petabytes. Elastic Clusters comprises one or more shards each of which has its own compute and storage volume. It is highly available across three Availability Zones (AZs) by default, with six copies of your data replicated across these three AZs. You can create Elastic Clusters using the Amazon DocumentDB API, AWS SDK, AWS CLI, AWS CloudFormation, or the AWS console.

Scale Workloads with Little to No Impact – With Elastic Clusters, your database can scale to millions of operations with little to no downtime or performance impact.

Integration with Other AWS Services – Elastic Clusters integrates with other AWS services in the same way Amazon DocumentDB does today. First, you can monitor the health and performance of your Elastic Clusters using Amazon CloudWatch. Second, you can set up authentication and authorization for resources such as clusters through AWS Identity and Access Management (IAM) users and roles and use Amazon Virtual Private Cloud (Amazon VPC) for secure VPC-only connections. Last, you can use AWS Glue to import and export data from and to other AWS services such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, and Amazon OpenSearch Service.

Getting Started with Elastic Clusters
Previously, I mentioned that you can use either the AWS console, AWS CLI, or AWS SDK to create Elastic Clusters. In the examples below, we will look at how you can create a cluster, scale up or out, and scale in or down using the AWS CLI:

Create a Cluster
When creating a cluster, you will specify the vCPUs that you want for your Elastic Clusters at provisioning. With the size of vCPUs that you provision, you will also get a proportionate amount of memory, expressed in vCPUs. Elastic Clusters automatically provisions the necessary infrastructure (shards and instances) on your behalf.
aws docdb-elastic create-cluster
--cluster-name foo
--shard-capacity 2
--shard-count 4
--auth-type PLAIN_TEXT
--admin-user-name docdbelasticadmin
--admin-user-password password

Scale Up or Out
If you need more compute and storage to handle an increase in traffic, modify the shard-count parameter. Elastic Clusters scales the underlying infrastructure up or out to give you additional compute and storage capacity.
aws docdb-elastic update-cluster
--cluster-arn foo-arn
--shard-count 8

Scale In or Down
If you no longer need the compute and storage that you currently have provisioned, either due to a decline in database traffic or the fact that you originally over-provisioned, modify the shard-count parameter. Elastic Clusters scales the underlying infrastructure in or down.
aws docdb-elastic update-cluster
--cluster-arn foo-arn
--shard-count 4

General Availability of Elastic Clusters for Amazon DocumentDB
Amazon DocumentDB Elastic Clusters is now available in all AWS Regions where Amazon DocumentDB is available, except China and AWS GovCloud. To learn more, visit the Amazon DocumentDB page.

Veliswa x

New – Announcing Automated Data Preparation for Amazon QuickSight Q

Post Syndicated from Veliswa Boya original

In this post that was published in September 2021, Jeff Barr announced general availability of Amazon QuickSight Q. To recap, Amazon QuickSight Q is a natural language query capability that lets business users ask simple questions of their data.

QuickSight Q is powered by machine learning (ML), providing self-service analytics by allowing you to query your data using plain language and therefore eliminating the need to fiddle with dashboards, controls, and calculations. With last year’s announcement of QuickSight Q, you can ask simple questions like “who had the highest sales in EMEA in 2021” and get your answers (with relevant visualizations like graphs, maps, or tables) in seconds.

Data used for analytics is often stored in a data warehouse like Amazon Redshift, and these unfortunately tend to be optimized for programmatic access via SQL rather than for natural language interaction. Furthermore, BI teams, understandably, tend to optimize data sources for consumption by dashboard authors, BI engineers, and other data teams, therefore using technical naming conventions that are optimized for dashboards (for example, “CUST_ID” instead of “Customer”) and SQL queries. These technical naming conventions are not intuitive to be used by business users.
To solve this, BI teams spend hours manually translating technical names into commonly used business language names to prepare the data for natural language questions.

Today, I’m excited to announce automated data preparation for Amazon QuickSight Q. Automated data preparation utilizes machine learning to infer semantic information about data and adds it to datasets as metadata about the columns (fields), making it faster for you to prepare data in order to support natural language questions.

A Quick Overview of Topics in QuickSight Q
Topics became available with the introduction of QuickSight Q. Topics are a collection of one or more datasets that represent a subject area that your business users can ask questions about. Looking at the example mentioned earlier (“who had the highest sales in EMEA in 2021”), one or more datasets (for example, a Sales/Regional Sales dataset) would be selected during the creation of this Topic.

As the author, once the Topic is created:

  • You would spend time selecting the most relevant columns from the dataset to add to the Topic (for example, excluding time_stamp, date_stamp columns, etc.). This can be challenging because without visibility to usage data of columns in dashboards and reports, you can find it hard to objectively decide which columns are most relevant to your business users to include in a Topic.
  • You would then spend hours reviewing the data and manually curating it to set configurations that are specific to natural language (for example, add “Area” as a synonym for the “Region” column).
  • Lastly, you would spend time formatting the data in order to ensure that it is more useful when presented.
  • QuickSight Q Topic

    QuickSight Q Topic

How Does Automated Data Preparation for Amazon QuickSight Q Work?
Creating from Analysis: The new automated data preparation for Amazon QuickSight Q saves time by enabling the capability to create a Topic from analysis and therefore saving you the hours that you would spend doing all the translation by automatically choosing user-friendly names and synonyms based on ML-trained models that seek to find synonyms and common terms for the data field in question. Moreover, instead of you selecting the most relevant columns, automated data preparation for Amazon QuickSight Q automatically selects high-value columns based on how they are used in the analysis. It then binds the Topic to this existing analysis’ dataset and prepares an index of unique string values within the data to enable natural language search.

Automated Field Selection and Classification: I mentioned earlier that automated data preparation for Amazon QuickSight Q selects high value columns, but how does it know which columns are high-value? Automated data preparation for Amazon QuickSight Q automates column selection based on signals from existing QuickSight assets, such as reports or dashboards, to help you create a Topic that is relevant to your business users. In addition to selecting high-value fields from a dataset, automated data preparation for Amazon QuickSight Q also imports new calculated fields that the author has created in the analysis, thereby not requiring them to recreate these in a Topic.

Automated Language Settings: At the beginning of this article, I talked about technical naming conventions that are not intuitive for business users. Now, instead of you spending time translating these technical names, column names are automatically updated with friendly names and synonyms using common terms. Looking at our Sales dataset example, CUST_ID has been assigned a friendly name, “Customer”, and a number of synonyms. Synonyms will now be added automatically to columns (with the option to customize further) to support a wide vocabulary that may be relevant to your business users.

Friendly names & Synonyms for columns

Friendly Names & Synonyms for Columns

Automated Metadata Settings: Automated data preparation for Amazon QuickSight Q detects Semantic Type of a column based on the column values and updates the corresponding configuration automatically. Formats for values will now be set to be used if a particular column is presented in the answer. These formats are derived from formats that you may have defined in an analysis.

Semantic Type Settings

Semantic Type Settings

Available Today
Automated Data Preparation for Amazon QuickSight Q is available today in all AWS Regions where QuickSight Q is available. To learn more, visit the Amazon QuickSight Q page. Join the QuickSight Community to ask, answer, and learn with others in the QuickSight Community.

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