Post Syndicated from BeardedTinker original https://www.youtube.com/watch?v=XdN_9Mf7W7E
[$] Using Common Lisp in Emacs
Post Syndicated from jake original https://lwn.net/Articles/951090/
Lisp
is one of the oldest programming languages still in use today, but it has
evolved in multiple directions over its more than 60-year history. Two of
the more prominent descendants, Common Lisp and Emacs Lisp (or Elisp),
are fairly closely related at some level, but there is still something of a
divide between them. Some recent discussion in the emacs-devel mailing
list have shown that some elements from Common Lisp are not completely
welcome in
Elisp—at least in the code that is maintained by the Emacs project itself.
Google Brings TPU v5e AI Board to SC23
Post Syndicated from Patrick Kennedy original https://www.servethehome.com/google-brings-tpu-v5e-ai-board-to-sc23/
Google brought its TPU v5e AI board to SC23 being watched over by a security guard so that attendees can see its AI powerhouse chip
The post Google Brings TPU v5e AI Board to SC23 appeared first on ServeTheHome.
Нова книга пише пътна карта срещу корупцията в България
Post Syndicated from Екип на Биволъ original https://bivol.bg/karabinov_korupcia_kniga.html
вторник 14 ноември 2023
Корупцията е опредялана като основната пречка пред развтието на България през последните поне 30 години. Има ли как да се изкорени “навика” да искаме и даваме подкуп, да назначаваме свои…
Streaming and longer context lengths for LLMs on Workers AI
Post Syndicated from Jesse Kipp original http://blog.cloudflare.com/workers-ai-streaming/
Workers AI is our serverless GPU-powered inference platform running on top of Cloudflare’s global network. It provides a growing catalog of off-the-shelf models that run seamlessly with Workers and enable developers to build powerful and scalable AI applications in minutes. We’ve already seen developers doing amazing things with Workers AI, and we can’t wait to see what they do as we continue to expand the platform. To that end, today we’re excited to announce some of our most-requested new features: streaming responses for all Large Language Models (LLMs) on Workers AI, larger context and sequence windows, and a full-precision Llama-2 model variant.
If you’ve used ChatGPT before, then you’re familiar with the benefits of response streaming, where responses flow in token by token. LLMs work internally by generating responses sequentially using a process of repeated inference — the full output of a LLM model is essentially a sequence of hundreds or thousands of individual prediction tasks. For this reason, while it only takes a few milliseconds to generate a single token, generating the full response takes longer, on the order of seconds. The good news is we can start displaying the response as soon as the first tokens are generated, and append each additional token until the response is complete. This yields a much better experience for the end user — displaying text incrementally as it’s generated not only provides instant responsiveness, but also gives the end-user time to read and interpret the text.
As of today, you can now use response streaming for any LLM model in our catalog, including the very popular Llama-2 model. Here’s how it works.
Server-sent events: a little gem in the browser API
Server-sent events are easy to use, simple to implement on the server side, standardized, and broadly available across many platforms natively or as a polyfill. Server-sent events fill a niche of handling a stream of updates from the server, removing the need for the boilerplate code that would otherwise be necessary to handle the event stream.
| Easy-to-use | Streaming | Bidirectional | |
|---|---|---|---|
| fetch | ✅ | ||
| Server-sent events | ✅ | ✅ | |
| Websockets | ✅ | ✅ |
To get started using streaming on Workers AI’s text generation models with server-sent events, set the “stream” parameter to true in the input of request. This will change the response format and mime-type to text/event-stream.
Here’s an example of using streaming with the REST API:
curl -X POST \
"https://api.cloudflare.com/client/v4/accounts/<account>/ai/run/@cf/meta/llama-2-7b-chat-int8" \
-H "Authorization: Bearer <token>" \
-H "Content-Type:application/json" \
-d '{ "prompt": "where is new york?", "stream": true }'
data: {"response":"New"}
data: {"response":" York"}
data: {"response":" is"}
data: {"response":" located"}
data: {"response":" in"}
data: {"response":" the"}
...
data: [DONE]And here’s an example using a Worker script:
import { Ai } from "@cloudflare/ai";
export default {
async fetch(request, env, ctx) {
const ai = new Ai(env.AI, { sessionOptions: { ctx: ctx } });
const stream = await ai.run(
"@cf/meta/llama-2-7b-chat-int8",
{ prompt: "where is new york?", stream: true }
);
return new Response(stream,
{ headers: { "content-type": "text/event-stream" } }
);
}
}If you want to consume the output event-stream from this Worker in a browser page, the client-side JavaScript is something like:
const source = new EventSource("/worker-endpoint");
source.onmessage = (event) => {
if(event.data=="[DONE]") {
// SSE spec says the connection is restarted
// if we don't explicitly close it
source.close();
return;
}
const data = JSON.parse(event.data);
el.innerHTML += data.response;
}You can use this simple code with any simple HTML page, complex SPAs using React or other Web frameworks.
This creates a much more interactive experience for the user, who now sees the page update as the response is incrementally created, instead of waiting with a spinner until the entire response sequence has been generated. Try it out streaming on ai.cloudflare.com.
Workers AI supports streaming text responses for the Llama-2 model and any future LLM models we are adding to our catalog.
But this is not all.
Higher precision, longer context and sequence lengths
Another top request we heard from our community after the launch of Workers AI was for longer questions and answers in our Llama-2 model. In LLM terminology, this translates to higher context length (the number of tokens the model takes as input before making the prediction) and higher sequence length (the number of tokens the model generates in the response.)
We’re listening, and in conjunction with streaming, today we are adding a higher 16-bit full-precision Llama-2 variant to the catalog, and increasing the context and sequence lengths for the existing 8-bit version.
| Model | Context length (in) | Sequence length (out) |
|---|---|---|
| @cf/meta/llama-2-7b-chat-int8 | 2048 (768 before) | 1800 (256 before) |
| @cf/meta/llama-2-7b-chat-fp16 | 3072 | 2500 |
Streaming, higher precision, and longer context and sequence lengths provide a better user experience and enable new, richer applications using large language models in Workers AI.
Check the Workers AI developer documentation for more information and options. If you have any questions or feedback about Workers AI, please come see us in the Cloudflare Community and the Cloudflare Discord.
If you are interested in machine learning and serverless AI, the Cloudflare Workers AI team is building a global-scale platform and tools that enable our customers to run fast, low-latency inference tasks on top of our network. Check our jobs page for opportunities.
Backblaze Drive Stats for Q3 2023
Post Syndicated from Andy Klein original https://www.backblaze.com/blog/backblaze-drive-stats-for-q3-2023/
At the end of Q3 2023, Backblaze was monitoring 263,992 hard disk drives (HDDs) and solid state drives (SSDs) in our data centers around the world. Of that number, 4,459 are boot drives, with 3,242 being SSDs and 1,217 being HDDs. The failure rates for the SSDs are analyzed in the SSD Edition: 2023 Drive Stats review.
That leaves us with 259,533 HDDs that we’ll focus on in this report. We’ll review the quarterly and lifetime failure rates of the data drives as of the end of Q3 2023. Along the way, we’ll share our observations and insights on the data presented, and, for the first time ever, we’ll reveal the drive failure rates broken down by data center.
Q3 2023 Hard Drive Failure Rates
At the end of Q3 2023, we were managing 259,533 hard drives used to store data. For our review, we removed 449 drives from consideration as they were used for testing purposes, or were drive models which did not have at least 60 drives. This leaves us with 259,084 hard drives grouped into 32 different models.
The table below reviews the annualized failure rate (AFR) for those drive models for the Q3 2023 time period.
Notes and Observations on the Q3 2023 Drive Stats
- The 22TB drives are here: At the bottom of the list you’ll see the WDC 22TB drives (model: WUH722222ALE6L4). A Backblaze Vault of 1,200 drives (plus four) is now operational. The 1,200 drives were installed on September 29, so they only have one day of service each in this report, but zero failures so far.
- The old get bolder: At the other end of the time-in-service spectrum are the 6TB Seagate drives (model: ST6000DX000) with an average of 101 months in operation. This cohort had zero failures in Q3 2023 with 883 drives and a lifetime AFR of 0.88%.
- Zero failures: In Q3, six different drive models managed to have zero drive failures during the quarter. But only the 6TB Seagate, noted above, had over 50,000 drive days, our minimum standard for ensuring we have enough data to make the AFR plausible.
- One failure: There were four drive models with one failure during Q3. After applying the 50,000 drive day metric, two drives stood out:
- WDC 16TB (model: WUH721816ALE6L0) with a 0.15% AFR.
- Toshiba 14TB (model: MG07ACA14TEY) with a 0.63% AFR.
The Quarterly AFR Drops
In Q3 2023, quarterly AFR for all drives was 1.47%. That was down from 2.2% in Q2 and also down from 1.65% a year ago. The quarterly AFR is based on just the data in that quarter, so it can often fluctuate from quarter to quarter.
In our Q2 2023 report, we suspected the 2.2% for the quarter was due to the overall aging of the drive fleet and in particular we pointed a finger at specific 8TB, 10TB, and 12TB drive models as potential culprits driving the increase. That prediction fell flat in Q3 as nearly two-thirds of drive models experienced a decreased AFR quarter over quarter from Q2 and any increases were minimal. This included our suspect 8TB, 10TB, and 12TB drive models.
It seems Q2 was an anomaly, but there was one big difference in Q3: we retired 4,585 aging 4TB drives. The average age of the retired drives was just over eight years, and while that was a good start, there’s another 28,963 4TB drives to go. To facilitate the continuous retirement of aging drives and make the data migration process easy and safe we use CVT, our awesome in-house data migration software which we’ll cover at another time.
A Hot Summer and the Drive Stats Data
As anyone should in our business, Backblaze continuously monitors our systems and drives. So, it was of little surprise to us when the folks at NASA confirmed the summer of 2023 as Earth’s hottest on record. The effects of this record-breaking summer showed up in our monitoring systems in the form of drive temperature alerts. A given drive in a storage server can heat up for many reasons: it is failing; a fan in the storage server has failed; other components are producing additional heat; the air flow is somehow restricted; and so on. Add in the fact that the ambient temperature within a data center often increases during the summer months, and you can get more temperature alerts.
In reviewing the temperature data for our drives in Q3, we noticed that a small number of drives exceeded the maximum manufacturer’s temperature for at least one day. The maximum temperature for most drives is 60°C, except for the 12TB, 14TB, and 16TB Toshiba drives which have a maximum temperature of 55°C. Of the 259,533 data drives in operation in Q3, there were 354 individual drives (0.0013%) that exceeded their maximum manufacturer temperature. Of those only two drives failed, leaving 352 drives which were still operational as of the end of Q3.
While temperature fluctuation is part of running data centers and temp alerts like these aren’t unheard of, our data center teams are looking into the root causes to ensure we’re prepared for the inevitability of increasingly hot summers to come.
Will the Temperature Alerts Affect Drive Stats?
The two drives which exceeded their maximum temperature and failed in Q3 have been removed from the Q3 AFR calculations. Both drives were 4TB Seagate drives (model: ST4000DM000). Given that the remaining 352 drives which exceeded their temperature maximum did not fail in Q3, we have left them in the Drive Stats calculations for Q3 as they did not increase the computed failure rates.
Beginning in Q4, we will remove the 352 drives from the regular Drive Stats AFR calculations and create a separate cohort of drives to track that we’ll name Hot Drives. This will allow us to track the drives which exceeded their maximum temperature and compare their failure rates to those drives which operated within the manufacturer’s specifications. While there are a limited number of drives in the Hot Drives cohort, it could give us some insight into whether drives being exposed to high temperatures could cause a drive to fail more often. This heightened level of monitoring will identify any increase in drive failures so that they can be detected and dealt with expeditiously.
New Drive Stats Data Fields in Q3
In Q2 2023, we introduced three new data fields that we started populating in the Drive Stats data we publish: vault_id, pod_id, and is_legacy_format. In Q3, we are adding three more fields into each drive records as follows:
datacenter: The Backblaze data center where the drive is installed, currently one of these values:ams5,iad1,phx1,sac0, andsac2.cluster_id: The name of a given collection of storage servers logically grouped together to optimize system performance. Note: At this time thecluster_idis not always correct, we are working on fixing that.pod_slot_num: The physical location of a drive within a storage server. The specific slot differs based on the storage server type and capacity: Backblaze (45 drives), Backblaze (60 drives), Dell (26 drives), or Supermicro (60 drives). We’ll dig into these differences in another post.
With these additions, the new schema beginning in Q3 2023 is:
dateserial_numbermodelcapacity_bytesfailuredatacenter(Q3)cluster_id(Q3)vault_id(Q2)pod_id(Q2)pod_slot_num(Q3)is_legacy_format(Q2)smart_1_normalizedsmart_1_raw- The remaining SMART value pairs (as reported by each drive model)
Beginning in Q3, these data data fields have been added to the publicly available Drive Stats files that we publish each quarter.
Failure Rates by Data Center
Now that we have the data center for each drive we can compute the AFRs for the drives in each data center. Below you’ll find the AFR for each of five data centers for Q3 2023.
Notes and Observations
- Null?: The drives which reported a null or blank value for their data center are grouped in four Backblaze vaults. David, the Senior Infrastructure Software Engineer for Drive Stats, described the process of how we gather all the parts of the Drive Stats data each day. The TL:DR is that vaults can be too busy to respond at the moment we ask, and since the data center field is nice-to-have data, we get a blank field. We can go back a day or two to find the data center value, which we will do in the future when we report this data.
- sac0?: sac0 has the highest AFR of all of the data centers, but it also has the oldest drives—nearly twice as old, on average, versus the next closest in data center, sac2. As discussed previously, drive failures do seem to follow the “bathtub curve”, although recently we’ve seen the curve start out flatter. Regardless, as drive models age, they do generally fail more often. Another factor could be that sac0, and to a lesser extent sac2, has some of the oldest Storage Pods, including a handful of 45-drive units. We are in the process of using CVT to replace these older servers while migrating from 4TB to 16TB and larger drives.
- iad1: The iad data center is the foundation of our eastern region and has been growing rapidly since coming online about a year ago. The growth is a combination of new data and customers using our cloud replication capability to automatically make a copy of their data in another region.
- Q3 Data: This chart is for Q3 data only and includes all the data drives, including those with less than 60 drives per model. As we track this data over the coming quarters, we hope to get some insight into whether different data centers really have different drive failure rates, and, if so, why.
Lifetime Hard Drive Failure Rates
As of September 30, 2023, we were tracking 259,084 hard drives used to store customer data. For our lifetime analysis, we collect the number of drive days and the number of drive failures for each drive beginning from the time a drive was placed into production in one of our data centers. We group these drives by model, then sum up the drive days and failures for each model over their lifetime. That chart is below.
One of the most important columns on this chart is the confidence interval, which is the difference between the low and high AFR confidence levels calculated at 95%. The lower the value, the more certain we are of the AFR stated. We like a confidence interval to be 0.5% or less. When the confidence interval is higher, that is not necessarily bad, it just means we either need more data or the data is somewhat inconsistent.
The table below contains just those drive models which have a confidence interval of less than 0.5%. We have sorted the list by drive size and then by AFR.
The 4TB, 6TB, 8TB, and some of the 12TB drive models are no longer in production. The HGST 12TB models in particular can still be found, but they have been relabeled as Western Digital and given alternate model numbers. Whether they have materially changed internally is not known, at least to us.
One final note about the lifetime AFR data: you might have noticed the AFR for all of the drives hasn’t changed much from quarter to quarter. It has vacillated between 1.39% to 1.45% percent for the last two years. Basically, we have lots of drives with lots of time-in-service so it is hard to move the needle up or down. While the lifetime stats for individual drive models can be very useful, the lifetime AFR for all drives will probably get less and less interesting as we add more and more drives. Of course, a few hundred thousand drives that never fail could arrive, so we will continue to calculate and present the lifetime AFR.
The Hard Drive Stats Data
The complete data set used to create the information used in this review is available on our Hard Drive Stats Data webpage. You can download and use this data for free for your own purpose. All we ask are three things: 1) you cite Backblaze as the source if you use the data, 2) you accept that you are solely responsible for how you use the data, and 3) you do not sell this data to anyone; it is free.
Good luck and let us know if you find anything interesting.
The post Backblaze Drive Stats for Q3 2023 appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.
Security updates for Tuesday
Post Syndicated from corbet original https://lwn.net/Articles/951311/
Security updates have been issued by Debian (postgresql-11, postgresql-13, and postgresql-15), Fedora (chromium, optipng, and radare2), Scientific Linux (plexus-archiver and python), Slackware (tigervnc), SUSE (apache2, containerized-data-importer, kernel-firmware-nvidia-gspx-G06, nvidia-open- driver-G06-signed, postgresql, postgresql15, postgresql16, postgresql12, postgresql13, python-Django1, squashfs, and xterm), and Ubuntu (firefox and memcached).
How .tk Became a TLD for Scammers
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/11/how-tk-became-a-tld-for-scammers.html
Sad story of Tokelau, and how its top-level domain “became the unwitting host to the dark underworld by providing a never-ending supply of domain names that could be weaponized against internet users. Scammers began using .tk websites to do everything from harvesting passwords and payment information to displaying pop-up ads or delivering malware.”
The serverless attendee’s guide to AWS re:Invent 2023
Post Syndicated from Marcia Villalba original https://aws.amazon.com/blogs/compute/the-serverless-attendees-guide-to-aws-reinvent-2023/
AWS re:Invent 2023 is fast approaching, bringing together tens of thousands of Builders in Las Vegas in November. However, even if you can’t attend in person, you can catch up with sessions on-demand.
Breakout sessions are lecture-style 60-minute informative sessions presented by AWS experts, customers, or partners. These sessions cover beginner (100 level) topics to advanced and expert (300–400 level) topics. The sessions are recorded and uploaded a few days after to the AWS Events YouTube channel.
This post shares the “must watch” breakout sessions related to serverless architectures and services.
Sessions related to serverless architecture
SVS401 | Best practices for serverless developers
Provides architectural best practices, optimizations, and useful shortcuts that experts use to build secure, high-scale, and high-performance serverless applications.
Chris Munns, Startup Tech Leader, AWS
Julian Wood, Principal Developer Advocate, AWS
SVS305 | Refactoring to serverless
Shows how you can refactor your application to serverless with real-life examples.
Gregor Hohpe, Senior Principal Evangelist, AWS
Sindhu Pillai, Senior Solutions Architect, AWS
SVS308 | Building low-latency, event-driven applications
Explores building serverless web applications for low-latency and event-driven support. Marvel Snap share how they achieve low-latency in their games using serverless technology.
Marcia Villalba, Principal Developer Advocate, AWS
Brenna Moore, Second Dinner
SVS309 | Improve productivity by shifting more responsibility to developers
Learn about approaches to accelerate serverless development with faster feedback cycles, exploring best practices and tools. Watch a live demo featuring an improved developer experience for building serverless applications while complying with enterprise governance requirements.
Heeki Park, Principal Solutions Architect, AWS
Sam Dengler, Capital One
GBL203-ES | Building serverless-first applications with MAPFRE
This session is delivered in Spanish. Learn what modern, serverless-first applications are and how to implement them with services such as AWS Lambda or AWS Fargate. Find out how MAPFRE have adopted and implemented a serverless strategy.
Jesus Bernal, Senior Solutions Architect, AWS
Iñigo Lacave, MAPFRE
Mat Jovanovic, MAPFRE
Sessions related to AWS Lambda
BOA311 | Unlocking serverless web applications with AWS Lambda Web Adapter
Learn about the AWS Lambda Web Adapter and how it integrates with familiar frameworks and tools. Find out how to migrate existing web applications to serverless or create new applications using AWS Lambda.
Betty Zheng, Senior Developer Advocate, AWS
Harold Sun, Senior Solutions Architect, AWS
OPN305 | The pragmatic serverless Python developer
Covers an opinionated approach to setting up a serverless Python project, including testing, profiling, deployments, and operations. Learn about many open source tools, including Powertools for AWS Lambda—a toolkit that can help you implement serverless best practices and increase developer velocity.
Heitor Lessa, Principal Solutions Architect, AWS
Ran Isenberg, CyberArk
XNT301 | Build production-ready serverless .NET apps with AWS Lambda
Explores development and architectural best practices when building serverless applications with .NET and AWS Lambda, including when to run ASP.NET on Lambda, code structure, and using native AOT to massively increase performance.
James Eastham, Senior Cloud Architect, AWS
Craig Bossie, Solutions Architect, AWS
COM306 | “Rustifying” serverless: Boost AWS Lambda performance with Rust
Discover how to deploy Rust functions using AWS SAM and cargo-lambda, facilitating a smooth development process from your local machine. Explore how to integrate Rust into Python Lambda functions effortlessly using tools like PyO3 and maturin, along with the AWS SDK for Rust. Uncover how Rust can optimize Lambda functions, including the development of Lambda extensions, all without requiring a complete rewrite of your existing code base.
Efi Merdler-Kravitz, Cloudex
COM305 | Demystifying and mitigating AWS Lambda cold starts
Examines the Lambda initialization process at a low level, using benchmarks comparing common architectural patterns, and then benchmarking various RAM configurations and payload sizes. Next, measure and discuss common mistakes that can increase initialization latency, explore and understand proactive initialization, and learn several strategies you can use to thaw your AWS Lambda cold starts.
AJ Stuyvenberg, Datadog
Sessions related to event-driven architecture
API302 | Building next gen applications with event driven architecture
Learn about common integration patterns and discover how you can use AWS messaging services to connect microservices and coordinate data flow using minimal custom code. Learn and plan for idempotency, handling duplicating events and building resiliency into your architectures.
Eric Johnson, Principal Developer Advocate, AWS
API303 | Navigating the journey of serverless event-driven architecture
Learn about the journey businesses undertake when adopting EDAs, from initial design and implementation to ongoing operation and maintenance. The session highlights the many benefits EDAs can offer organizations and focuses on areas of EDA that are challenging and often overlooked. Through a combination of patterns, best practices, and practical tips, this session provides a comprehensive overview of the opportunities and challenges of implementing EDAs and helps you understand how you can use them to drive business success.
David Boyne, Senior Developer Advocate, AWS
API309 | Advanced integration patterns and trade-offs for loosely coupled apps
In this session, learn about common design trade-offs for distributed systems, how to navigate them with design patterns, and how to embed those patterns in your cloud automation.
Dirk Fröhner, Principal Solutions Architect, AWS
Gregor Hohpe, Senior Principal Evangelist, AWS
SVS205 | Getting started building serverless event-driven applications
Learn about the process of prototyping a solution from concept to a fully featured application that uses Amazon API Gateway, AWS Lambda, Amazon EventBridge, AWS Step Functions, Amazon DynamoDB, AWS Application Composer, and more. Learn why serverless is a great tool set for experimenting with new ideas and how the extensibility and modularity of serverless applications allow you to start small and quickly make your idea a reality.
Emily Shea, Head of Application Integration Go-to-Market, AWS
Naren Gakka, Solutions Architect, AWS
API206 | Bringing workloads together with event-driven architecture
Attend this session to learn the steps to bring your existing container workloads closer together using event-driven architecture with minimal code changes and a high degree of reusability. Using a real-life business example, this session walks through a demo to highlight the power of this approach.
Dhiraj Mahapatro, Principal Solutions Architect, AWS
Nicholas Stumpos, JPMorgan Chase & Co
COM301 | Advanced event-driven patterns with Amazon EventBridge
Gain an understanding of the characteristics of EventBridge and how it plays a pivotal role in serverless architectures. Learn the primary elements of event-driven architecture and some of the best practices. With real-world use cases, explore how the features of EventBridge support implementing advanced architectural patterns in serverless.
Sheen Brisals, The LEGO Group
Sessions related to serverless APIs
SVS301 | Building APIs: Choosing the best API solution and strategy for your workloads
Learn about access patterns and how to evaluate the best API technology for your applications. The session considers the features and benefits of Amazon API Gateway, AWS AppSync, Amazon VPC Lattice, and other options.
Josh Kahn, Tech Leader Serverless, AWS
Arthi Jaganathan, Principal Solutions Architect, AWS
SVS323 | I didn’t know Amazon API Gateway did that
This session provides an introduction to Amazon API Gateway and the problems it solves. Learn about the moving parts of API Gateway and how it works, including common and not-so-common use cases. Discover why you should use API Gateway and what it can do.
Eric Johnson, Principal Developer Advocate, AWS
FWM201 | What’s new with AWS AppSync for enterprise API developers
Join this session to learn about all the exciting new AWS AppSync features released this year that make it even more seamless for API developers to realize the benefits of GraphQL for application development.
Michael Liendo, Senior Developer Advocate, AWS
Brice Pellé, Principal Product Manager, AWS
FWM204 | Implement real-time event patterns with WebSockets and AWS AppSync
Learn how the PGA Tour uses AWS AppSync to deliver real-time event updates to their app users; review new features, like enhanced filtering options and native integration with Amazon EventBridge; and provide a sneak peek at what’s coming next.
Ryan Yanchuleff, Senior Solutions Architect, AWS
Bill Fine, Senior Product Manager, AWS
David Provan, PGA Tour
Sessions related to AWS Step Functions
API401 | Advanced workflow patterns and business processes with AWS Step Functions
Learn about architectural best practices and repeatable patterns for building workflows and cost optimizations, and discover handy cheat codes that you can use to build secure, high-scale, high-performance serverless applications
Ben Smith, Principal Developer Advocate, AWS
BOA304 | Using AI and serverless to automate video production
Learn how to use Step Functions to build workflows using AI services and how to use Amazon EventBridge real-time events.
Marcia Villalba, Principal Developer Advocate, AWS
SVS204 | Building Serverlesspresso: Creating event-driven architectures
This session explores the design decisions that were made when building Serverlesspresso, how new features influenced the development process, and lessons learned when creating a production-ready application using this approach. Explore useful patterns and options for extensibility that helped in the design of a robust, scalable solution that costs about one dollar per day to operate. This session includes examples you can apply to your serverless applications and complex architectural challenges for larger applications.
James Beswick, Senior Manager Developer Advocacy, AWS
API310 | Scale interactive data analysis with Step Functions Distributed Map
Learn how to build a data processing or other automation once and readily scale it to thousands of parallel processes with serverless technologies. Explore how this approach simplifies development and error handling while improving speed and lowering cost. Hear from an AWS customer that refactored an existing machine learning application to use Distributed Map and the lessons they learned along the way.
Adam Wagner, Principal Solutions Architect, AWS
Roberto Iturralde, Vertex Pharmaceuticals
Sessions related to handling data using serverless services and serverless databases
SVS307 | Scaling your serverless data processing with Amazon Kinesis and Kafka
Explore how to build scalable data processing applications using AWS Lambda. Learn practical insights into integrating Lambda with Amazon Kinesis and Apache Kafka using their event-driven models for real-time data streaming and processing.
Julian Wood, Principal Developer Advocate, AWS
DAT410 | Advanced data modeling with Amazon DynamoDB
This session shows you advanced techniques to get the most out of DynamoDB. Learn how to “think in DynamoDB” by learning the DynamoDB foundations and principles for data modeling. Learn practical strategies and DynamoDB features to handle difficult use cases in your application.
Alex De Brie – Independent consultant
COM308 | Serverless data streaming: Amazon Kinesis Data Streams and AWS Lambda
Explore the intricacies of creating scalable, production-ready data streaming architectures using Kinesis Data Streams and Lambda. Delve into tips and best practices essential to navigating the challenges and pitfalls inherent to distributed systems that arise along the way, and observe how AWS services work and interact.
Anahit Pogosova, Solita
Additional resources
If you are attending the event, there are many chalk talks, workshops, and other sessions to visit. See ServerlessLand for a full list of all the serverless sessions and also the Serverless Hero, Danielle Heberling’s Serverless re:Invent attendee guide for her top picks.
Visit us in the AWS Village in the Expo Hall where you can find the Serverless and Containers booth and enjoy a free cup of coffee at Serverlesspresso.
For more serverless learning resources, visit Serverless Land.
Code Editor update: Support for HTML and mobile devices
Post Syndicated from Mel Farrington original https://www.raspberrypi.org/blog/html-code-editor/
Earlier this year, we launched our Code Editor, a free online tool to help make learning text-based programming simple and accessible for kids age 9 and up. We focus on supporting the needs of young people who are learning programming at school, in Code Clubs and CoderDojos, and at home.
Today, we have two exciting updates to share: support for web page projects with HTML/CSS, and an improved mobile and tablet experience.
What’s the Code Editor?
Learners can use the Code Editor to write and run code in a web browser without installing any additional software. The Editor is currently available as a beta version, and we’ve already received really positive comments:
“The Editor looks really nice! I have tried the Python part, and it is intuitive and concise. My little program worked no problem, and I am sure the Editor will be easy, intuitive, and quick to learn for the young [learners].”
— Volunteer in the CoderDojo community
Introducing HTML and CCS in the Code Editor
The Code Editor now supports the HTML and CSS web development languages, giving young people the ability to create and preview their own websites directly in the Editor interface. Learners can have their code and the preview panel side by side, and they can also preview their websites in a separate, larger tab.
We have embedded the Editor in our ‘Introduction to web‘ path on the Projects site. The path contains six HTML and CSS projects for beginners and helps them create fun websites like the ones shown here.
We want the Code Editor to be safe, age-appropriate, and suitable for use in classrooms or coding clubs. With this in mind, we have excluded certain functions, like being able to add links to external websites in the code. Rather than enabling image uploads, we provide a library of images when projects in our free learning paths contain images, in order to support multimedia projects safely.
Whether users are coding in Python or HTML/CSS, the Editor offers accessibility options so you can easily switch settings between light and dark mode, and between small, medium, and large text size. The text size feature is useful for people with visual impairments, as well as for educators who want to demonstrate something to a group of learners.
Improved experience for mobile and tablet devices
Our Code Editor now offers a new and improved experience for users of mobile and tablet devices. This improves access for learners in classrooms where tablets are used, and in low- and middle-income countries, where mobile phones are commonly used for digital learning.
The Editor now includes:
- A clearer and simpler navigation for small-screen devices
- Separate Menu, Code and Output/Preview tabs
- The same features on mobile/tablet devices as on desktop of laptop computers, such as responsive panels and the option to open HTML/CSS projects in a new tab
Try the Code Editor today
We’re continuing to develop the Code Editor and have more improvements planned. If you would like to try it out and provide us with your feedback, we’d love to hear what you think of our latest updates.
Code Editor developments have been made possible with generous support from Endless and the Cisco Foundation.
The post Code Editor update: Support for HTML and mobile devices appeared first on Raspberry Pi Foundation.
Detecting Speech and Music in Audio Content
Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/detecting-speech-and-music-in-audio-content-afd64e6a5bf8
Iroro Orife, Chih-Wei Wu and Yun-Ning (Amy) Hung
Introduction
When you enjoy the latest season of Stranger Things or Casa de Papel (Money Heist), have you ever wondered about the secrets to fantastic story-telling, besides the stunning visual presentation? From the violin melody accompanying a pivotal scene to the soaring orchestral arrangement and thunderous sound-effects propelling an edge-of-your-seat action sequence, the various components of the audio soundtrack combine to evoke the very essence of story-telling. To uncover the magic of audio soundtracks and further improve the sonic experience, we need a way to systematically examine the interaction of these components, typically categorized as dialogue, music and effects.
In this blog post, we will introduce speech and music detection as an enabling technology for a variety of audio applications in Film & TV, as well as introduce our speech and music activity detection (SMAD) system which we recently published as a journal article in EURASIP Journal on Audio, Speech, and Music Processing.
Like semantic segmentation for audio, SMAD separately tracks the amount of speech and music in each frame in an audio file and is useful in content understanding tasks during the audio production and delivery lifecycle. The detailed temporal metadata SMAD provides about speech and music regions in a polyphonic audio mixture are a first step for structural audio segmentation, indexing and pre-processing audio for the following downstream tasks. Let’s have a look at a few applications.
Practical use cases for speech & music activity
Audio dataset preparation
Speech & music activity is an important preprocessing step to prepare corpora for training. SMAD classifies & segments long-form audio for use in large corpora, such as
- musical segments for music information retrieval tasks (MIR).
- utterances for speech tasks like speaker diarization, emotion classification, semantic and phonetic transcription and translation.
Dialogue analysis & processing
- During encoding at Netflix, speech-gated loudness is computed for every audio master track and used for loudness normalization. Speech-activity metadata is thus a central part of accurate catalog-wide loudness management and improved audio volume experience for Netflix members.
- Similarly, algorithms for dialogue intelligibility, spoken-language-identification and speech-transcription are only applied to audio regions where there is measured speech.
Music information retrieval
- There are a few studio use cases where music activity metadata is important, including quality-control (QC) and at-scale multimedia content analysis and tagging.
- There are also inter-domain tasks like singer-identification and song lyrics transcription, which do not fit neatly into either speech or classical MIR tasks, but are useful for annotating musical passages with lyrics in closed captions and subtitles.
- Conversely, where neither speech nor music activity is present, such audio regions are estimated to have content classified as noisy, environmental or sound-effects.
Localization & Dubbing
Finally, there are post-production tasks, which take advantage of accurate speech segmentation at the the spoken utterance or sentence level, ahead of translation and dub-script generation. Likewise, authoring accessibility-features like Audio Description (AD) involves music and speech segmentation. The AD narration is typically mixed-in to not overlap with the primary dialogue, while music lyrics strongly tied to the plot of the story, are sometimes referenced by AD creators, especially for translated AD.
Our Approach to Speech and Music Activity Detection
Although the application of deep learning methods has improved audio classification systems in recent years, this data driven approach for SMAD requires large amounts of audio source material with audio-frame level speech and music activity labels. The collection of such fine-resolution labels is costly and labor intensive and audio content often cannot be publicly shared due to the copyright limitations. We address the challenge from a different angle.
Content, genre and languages
Instead of augmenting or synthesizing training data, we sample the large scale data available in the Netflix catalog with noisy labels. In contrast to clean labels, which indicate precise start and end times for each speech/music region, noisy labels only provide approximate timing, which may impact SMAD classification performance. Nevertheless, noisy labels allow us to increase the scale of the dataset with minimal manual efforts and potentially generalize better across different types of content.
Our dataset, which we introduced as TVSM (TV Speech and Music) in our publication, has a total number of 1608 hours of professionally recorded and produced audio. TVSM is significantly larger than other SMAD datasets and contains both speech and music labels at the frame level. TVSM also contains overlapping music and speech labels, and both classes have a similar total duration.
Training examples were produced between 2016 and 2019, in 13 countries, with 60% of the titles originating in the USA. Content duration ranged from 10 minutes to over 1 hour, across the various genres listed below.
The dataset contains audio tracks in three different languages, namely English, Spanish, and Japanese. The language distribution is shown in the figure below. The name of the episode/TV show for each sample remains unpublished. However, each sample has both a show-ID and a season-ID to help identify the connection between the samples. For instance, two samples from different seasons of the same show would share the same show ID and have different season IDs.
What constitutes music or speech?
To evaluate and benchmark our dataset, we manually labeled 20 audio tracks from various TV shows which do not overlap with our training data. One of the fundamental issues encountered during the annotation of our manually-labeled TVSM-test set, was the definition of music and speech. The heavy usage of ambient sounds and sound effects blurs the boundaries between active music regions and non-music. Similarly, switches between conversational speech and singing voices in certain TV genres obscure where speech starts and music stops. Furthermore, must these two classes be mutually exclusive? To ensure label quality, consistency, and to avoid ambiguity, we converged on the following guidelines for differentiating music and speech:
- Any music that is perceivable by the annotator at a comfortable playback volume should be annotated.
- Since sung lyrics are often included in closed-captions or subtitles, human singing voices should all be annotated as both speech and music.
- Ambient sound or sound effects without apparent melodic contours should not be annotated as music. Traditional phone bell, ringing, or buzzing without apparent melodic contours should not be annotated as music.
- Filled pauses (uh, um, ah, er), backchannels (mhm, uh-huh), sighing, and screaming should not be annotated as speech.
Audio format and preprocessing
All audio files were originally delivered from the post-production studios in the standard 5.1 surround format at 48 kHz sampling rate. We first normalize all files to an average loudness of −27 LKFS ± 2 LU dialog-gated, then downsample to 16 kHz before creating an ITU downmix.
Model Architecture
Our modeling choices take advantage of both convolutional and recurrent architectures, which are known to work well on audio sequence classification tasks, and are well supported by previous investigations. We adapted the SOTA convolutional recurrent neural network (CRNN) architecture to accommodate our requirements for input/output dimensionality and model complexity. The best model was a CRNN with three convolutional layers, followed by two bi-directional recurrent layers and one fully connected layer. The model has 832k trainable parameters and emits frame-level predictions for both speech and music with a temporal resolution of 5 frames per second.
For training, we leveraged our large and diverse catalog dataset with noisy labels, introduced above. Applying a random sampling strategy, each training sample is a 20 second segment obtained by randomly selecting an audio file and corresponding starting timecode offset on the fly. All models in our experiments were trained by minimizing binary cross-entropy (BCE) loss.
Evaluation
In order to understand the influence of different variables in our experimental setup, e.g. model architecture, training data or input representation variants like log-Mel Spectrogram versus per-channel energy normalization (PCEN), we setup a detailed ablation study, which we encourage the reader to explore fully in our EURASIP journal article.
For each experiment, we reported the class-wise F-score and error rate with a segment size of 10ms. The error rate is the summation of deletion rate (false negative) and insertion rate (false positive). Since a binary decision must be attained for music and speech to calculate the F-score, a threshold of 0.5 was used to quantize the continuous output of speech and music activity functions.
Results
We evaluated our models on four open datasets comprising audio data from TV programs, YouTube clips and various content such as concert, radio broadcasts, and low-fidelity folk music. The excellent performance of our models demonstrates the importance of building a robust system that detects overlapping speech and music and supports our assumption that a large but noisy-labeled real-world dataset can serve as a viable solution for SMAD.
Conclusion
At Netflix, tasks throughout the content production and delivery lifecycle work are most often interested in one part of the soundtrack. Tasks that operate on just dialogue, music or effects are performed hundreds of times a day, by teams around the globe, in dozens of different audio languages. So investments in algorithmically-assisted tools for automatic audio content understanding like SMAD, can yield substantial productivity returns at scale while minimizing tedium.
Additional Resources
We have made audio features and labels available via Zenodo. There is also GitHub repository with the following audio tools:
- Python code for data pre-processing, including scripts for 5.1 downmixing, Mel spectrogram generation, MFCCs generation, VGGish features generation, and the PCEN implementation.
- Python code for reproducing all experiments, including scripts of data loaders, model implementations, training and evaluation pipelines.
- Pre-trained models for each conducted experiment.
- Prediction outputs for all audio in the evaluation datasets.
Special thanks to the entire Audio Algorithms team, as well as Amir Ziai, Anna Pulido, and Angie Pollema.
Detecting Speech and Music in Audio Content was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.
Comic for 2023.11.14 – Game Of The Year
Post Syndicated from Explosm.net original https://explosm.net/comics/game-of-the-year
New Cyanide and Happiness Comic
Amazon MSK Serverless now supports Kafka clients written in all programming languages
Post Syndicated from Ali Alemi original https://aws.amazon.com/blogs/big-data/amazon-msk-serverless-now-supports-kafka-clients-written-in-all-programming-languages/
Amazon MSK Serverless is a cluster type for Amazon Managed Streaming for Apache Kafka (Amazon MSK) that is the most straightforward way to run Apache Kafka clusters without having to manage compute and storage capacity. With MSK Serverless, you can run your applications without having to provision, configure, or optimize clusters, and you pay for the data volume you stream and retain. MSK Serverless fully manages partitions, including monitoring and moving them to even load across a cluster.
With today’s launch, MSK Serverless now supports writes and reads from Kafka clients written in all programming languages. Administrators can simplify and standardize access control to Kafka resources using AWS Identity and Access Management (IAM). This support for IAM in Amazon MSK is based on SASL/OUATHBEARER, an open standard for authorization and authentication.
In this post, we show how you can connect your applications to MSK Serverless with minimal code changes using the open-sourced client helper libraries and code samples for popular languages, including Java, Python, Go, JavaScript, and .NET. Using IAM authentication and authorization is the preferred choice of many customers because you can secure Kafka resources just like you do with all other AWS services. Additionally, you get all the other benefits of IAM, such as temporary role-based credentials, precisely scoped permission policies, and more. Now you can use MSK Serverless with IAM-based authentication more broadly with the support for multiple languages.
Solution overview
You can get started by using IAM principals as identities for your Apache Kafka clients and define identity policies to provide them precisely scoped access permissions. For example, you can create an IAM user and a policy that allows the user to write to a specific Kafka topic but restricts access to other resources without worrying about managing Kafka ACLs. After you provide the identity policies with the necessary permissions, you can configure client applications to use the IAM authentication with minimal code changes.
The code changes allow your clients to use SASL/OAUTHBEARER, a Kafka supported token-based access mechanism, to pass the credentials required for IAM authentication. With OAUTHBEARER support, you can build clients that can work across both Amazon MSK and other Kafka environments. In this post, we show how you can make these code changes by using the provided code libraries and examples.
With this launch, Amazon MSK provides new code libraries for the following programming languages in the AWS GitHub repo:
The following diagram shows the conceptual process flow of using SASL/OAUTHBEARER with IAM access control for non-Java clients.
The workflow contains the following steps:
- The client generates an OAUTHBEARER token with the help of the provided library. The token contains a signed base64 encoded transformation of your IAM identity credentials.
- The client sends this to Amazon MSK using the bootstrap address along with its request to access Apache Kafka resources.
- The MSK Serverless cluster decodes the OATHBEARER token, validates the credentials, and checks if the client is authorized to perform the requested action according to the policy attached to the IAM identity.
- When the token expires, the client Kafka library automatically refreshes the token by making another call to the specified token provider.
Create IAM identities and policies
IAM access control for non-Java clients is supported for MSK Serverless clusters with no additional cost. Before you start, you need to configure the IAM identities and policies that define the client’s permissions to access resources on the cluster. The following is an example authorization policy for a cluster named MyTestCluster. To understand the semantics of the action and resource elements, see Semantics of actions and resources.
Configure the client
You should make code changes to your application that allow the clients to use SASL/OAUTHBEARER to pass the credentials required for IAM authentication. You also need to make sure the security group associated with your MSK Serverless cluster has an inbound rule allowing the traffic from the client applications in the associated VPCs to the TCP port number 9098.
You must use a Kafka client library that provides support for SASL with OAUTHBRARER authentication.
For this post, we use the Python programming language. We also use https://github.com/dpkp/kafka-python as our Kafka client library.
Amazon MSK provides you with a new code library per each language that generates the OAUTHBEARER token.
- To get started working with the Amazon MSK IAM SASL signer for Python with your Kafka client library, run the following command:
- Import the installed Amazon MSK IAM SASL signer library in your code:
- Next, your application code needs to define a token provider that wraps the function that generates new tokens:
- Specify
security_protocolasSASL_SSLandsasl_mechanismasoauthbearerin your Python Kafka client properties, and pass the token provider in the configuration object:
You are now finished with all the code changes. For more examples of generating auth tokens or for more troubleshooting tips, refer to the following GitHub repo.
Conclusion
MSK Serverless now supports writes and reads from Kafka clients written in all programming languages. You can run your applications without having to configure and manage the infrastructure or optimize clusters, and you pay for the data volume you stream and retain. MSK Serverless fully manages partitions, including monitoring, and ensures an even balance of partition distribution across brokers in the cluster (auto-balancing).
For further reading on Amazon MSK, visit the official product page.
About the author
Ali Alemi is a Streaming Specialist Solutions Architect at AWS. Ali advises AWS customers with architectural best practices and helps them design real-time analytics data systems that are reliable, secure, efficient, and cost-effective. He works backward from customer’s use cases and designs data solutions to solve their business problems. Prior to joining AWS, Ali supported several public sector customers and AWS consulting partners in their application modernization journey and migration to the Cloud.
Amazon MSK IAM authentication now supports all programming languages
Post Syndicated from Ali Alemi original https://aws.amazon.com/blogs/big-data/amazon-msk-iam-authentication-now-supports-all-programming-languages/
The AWS Identity and Access Management (IAM) authentication feature in Amazon Managed Streaming for Apache Kafka (Amazon MSK) now supports all programming languages. Administrators can simplify and standardize access control to Kafka resources using IAM. This support is based on SASL/OUATHBEARER, an open standard for authorization and authentication. Both Amazon MSK provisioned and serverless cluster types support the new Amazon MSK IAM expansion to all programming languages.
In this post, we show how you can connect your applications to MSK clusters with minimal code changes using the open-sourced client helper libraries and code samples for popular languages, including Java, Python, Go, JavaScript, and .NET. You can also use standard IAM access controls such as temporary role-based credentials and precisely scoped permission policies more broadly with the multiple language support on Amazon MSK.
For clients that need to connect from other VPCs to an MSK cluster, whether in a same or a different AWS account, you can enable multi-VPC private connectivity and cluster policy support. IAM access control via cluster policy helps you manage all access to the cluster and topics in one place. For example, you can control which IAM principals have write access to certain topics, and which principals can only read from them. Users who are using IAM client authentication can also add permissions for required kafka-cluster actions in the cluster resource policy.
Solution overview
You can get started by using IAM principals as identities for your Apache Kafka clients and define identity policies to provide them precisely scoped access permissions. After IAM authentication is enabled for your cluster, you can configure client applications to use the IAM authentication with minimal code changes.
The code changes allow your clients to use SASL/OAUTHBEARER, a Kafka supported token-based access mechanism, to pass the credentials required for IAM authentication. In this post, we show how you can make these code changes by using the provided code libraries and examples.
With this launch, new code libraries for the following programming languages are available in the AWS GitHub repo:
The following diagram shows the conceptual process flow of using SASL/OAUTHBEARER with IAM access control for non-Java clients.
The workflow contains the following steps:
- The client generates an OAUTHBEARER token with the help of the provided library. The token contains a signed base64 encoded transformation of your IAM identity credentials.
- The client sends this to Amazon MSK using the IAM bootstrap broker addresses along with its request to access Apache Kafka resources.
- The MSK broker decodes the OATHBEARER token, validates the credentials, and checks if the client is authorized to perform the requested action according to the policy attached to the IAM identity.
- When the token expires, the client Kafka library automatically refreshes the token by making another call to the specified token provider.
Create IAM identities and policies
IAM access control for non-Java clients is supported for MSK clusters with Kafka version 2.7.1 and above. Before you start, you need to configure the IAM identities and policies that define the client’s permissions to access resources on the cluster. The following is an example authorization policy for a cluster named MyTestCluster. To understand the semantics of the action and resource elements, see Semantics of actions and resources.
Set up the MSK cluster
You need to enable the IAM access control authentication scheme for your MSK provisioned cluster and wait until the cluster finishes updating and turns to the Active state. This is because SASL/OAUTHBEARER uses the same broker addresses for IAM authentication.
Configure the client
You should make code changes to your application that allow the clients to use SASL/OAUTHBEARER to pass the credentials required for IAM authentication. Next, update your application to use the bootstrap server addresses for IAM authentication. You also need to make sure the security group associated with your MSK cluster has an inbound rule allowing the traffic from the client applications in the same VPC as the cluster to the TCP port 9098.
You must use a Kafka client library that provides support for SASL with OAUTHBRARER authentication.
In this post, we use the JavaScript programming language. We also use https://github.com/tulios/kafkajs as our Kafka client library.
Amazon MSK provides you with a new code library per each language that generates the OAUTHBEARER token.
- To get started working with the Amazon MSK IAM SASL signer for JavaScript with your Kafka client library, run the following command:
- You need to import the installed Amazon MSK IAM SASL signer library in your code:
- Next, your application code needs to define a token provider that wraps the function that generates new tokens:
- Specify
security_protocolasSASL_SSLandsasl_mechanismasoauthbearerin your JavaScript Kafka client properties, and pass the token provider in the configuration object:
You are now finished with all the code changes. For more examples of generating auth tokens or for more troubleshooting tips, refer to the following GitHub repo.
Conclusion
IAM access control for Amazon MSK enables you to handle both authentication and authorization for your MSK cluster. This eliminates the need to use one mechanism for authentication and another for authorization. For example, when a client tries to write to your cluster, Amazon MSK uses IAM to check whether that client is an authenticated identity and also whether it is authorized to produce to your cluster.
With today’s launch, Amazon MSK IAM authentication now supports all programming languages. This means you can connect your applications in all languages without worrying about implementing separate authentication and authorization mechanisms. For workloads that require Amazon MSK multi-VPC private connectivity and cluster policy support, you can now simplify connectivity to your MSK brokers and manage all access to the cluster and topics in one place that is your cluster policy.
For further reading on Amazon MSK, visit the official product page.
About the author
Ali Alemi is a Streaming Specialist Solutions Architect at AWS. Ali advises AWS customers with architectural best practices and helps them design real-time analytics data systems that are reliable, secure, efficient, and cost-effective. He works backward from customer’s use cases and designs data solutions to solve their business problems. Prior to joining AWS, Ali supported several public sector customers and AWS consulting partners in their application modernization journey and migration to the cloud.
Amazon Bedrock now provides access to Meta’s Llama 2 Chat 13B model
Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/amazon-bedrock-now-provides-access-to-llama-2-chat-13b-model/
Today, we’re announcing the availability of Meta’s Llama 2 Chat 13B large language model (LLM) on Amazon Bedrock. With this launch, Amazon Bedrock becomes the first public cloud service to offer a fully managed API for Llama 2, Meta’s next-generation LLM. Now, organizations of all sizes can access Llama 2 Chat models on Amazon Bedrock without having to manage the underlying infrastructure. This is a step change in accessibility.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies, including AI21 Labs, Anthropic, Cohere, Stability AI, Amazon, and now Meta, along with a broad set of capabilities to build generative AI applications, simplifying the development while maintaining privacy and security. You can read more about Amazon Bedrock in Antje’s post here.
Llama 2 is a family of publicly available LLMs by Meta. The Llama 2 base model was pre-trained on 2 trillion tokens from online public data sources. According to Meta, the training of Llama 2 13B consumed 184,320 GPU/hour. That’s the equivalent of 21.04 years of a single GPU, not accounting for bissextile years.
Built on top of the base model, the Llama 2 Chat model is optimized for dialog use cases. It is fine-tuned with over 1 million human annotations (a technique known as reinforcement learning from human feedback or RLHF) and has undergone testing by Meta to identify performance gaps and mitigate potentially problematic responses in chat use cases, such as offensive or inappropriate responses.
To promote a responsible, collaborative AI innovation ecosystem, Meta established a range of resources for all who use Llama 2: individuals, creators, developers, researchers, academics, and businesses of any size. In particular, I like the Meta Responsible Use Guide, a resource for developers that provides best practices and considerations for building products powered by LLMs in a responsible manner, covering various stages of development from inception to deployment. This guide fits well in the set of AWS tools and resources to build AI responsibly.
You can now integrate the LLama 2 Chat model in your applications written in any programming language by calling the Amazon Bedrock API or using the AWS SDKs or the AWS Command Line Interface (AWS CLI).
Llama 2 Chat in action
Those of you who read the AWS News blog regularly know we like to show you the technologies we write about. So let’s write code to interact with Llama2.
I was lucky enough to talk at the AWS UG Perú Conf a few weeks ago. Jeff and Marcia were there too. Jeff opened the conference with an inspiring talk about generative AI, and he used a wall of generated images of llamas, the emblematic animal from Perú. So what better subject to talk about with Llama 2 Chat than llamas?
(And before writing code, I can’t resist sharing two photos of llamas I took during my visit to Machu Picchu)
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To get started with a new model on Bedrock, I first navigate to Amazon Bedrock on the console. I select Model access on the bottom left pane, then select the Edit button on the top right side, and enable access to the Llama 2 Chat model.
In the left navigation bar, under Playgrounds, I select Chat to interact with the model without writing any code.
Now that I know I can access the model, I open a code editor on my laptop. I assume you have the AWS Command Line Interface (AWS CLI) configured, which will allow the AWS SDK to locate your AWS credentials. I use Python for this demo, but I want to show that Bedrock can be called from any language. I also share a public gist with the same code sample written in the Swift programming language.
Returning to Python, I first run the ListFoundationModels API call to discover the modelId for Llama 2 Chat 13B.
import boto3
bedrock = boto3.client(service_name='bedrock', region_name='us-east-1')
listModels = bedrock.list_foundation_models(byProvider='meta')
print("\n".join(list(map(lambda x: f"{x['modelName']} : { x['modelId'] }", listModels['modelSummaries']))))
Running this code produces the list:
Llama 2 Chat 13B : meta.llama2-13b-chat-v1
I select meta-llama2-chat-13b model ID and write the code to send a prompt to the LLama 2 Chat 13B model.
import boto3
import json
llamaModelId = 'meta.llama2-13b-chat-v1'
prompt = "What is the difference between a llama and an alpaca?"
llamaPayload = json.dumps({
'prompt': prompt,
'max_gen_len': 512,
'top_p': 0.9,
'temperature': 0.2
})
bedrock_runtime = boto3.client(
service_name='bedrock-runtime',
region_name='us-east-1'
)
response = bedrock_runtime.invoke_model(
body=llamaPayload,
modelId=llamaModelId,
accept='application/json',
contentType='application/json'
)
body = response.get('body').read().decode('utf-8')
response_body = json.loads(body)
print(response_body['generation'].strip())The response is printed:
Llamas and alpacas are both members of the camelid family,
but they are different species with distinct physical and behavioral characteristics.
Here are some of the main differences: (...)I redacted the answer for brevity.
You can tune the different request parameters to see how they influence the response.
Optimizing your prompts
To get the best results out of the Llama 2 Chat model, you can experiment with a couple of prompt engineering techniques, such as using roles or chain-of-thought prompts.
Using roles, you tell the model how to behave by giving it a role and instructions before asking your question. For example:
Prompt: You are a teacher. You explain things for six-year old kids.
What is the difference between a llama and an alpaca?
Answer: Great question! Both llamas and alpacas are animals that live in South America, and they look a little bit similar. But there are some differences that we can talk about.
First, let's talk about their fur. Llamas have longer fur than alpacas. It's like they have a big, fluffy coat that helps them stay warm in the cold weather. Alpacas, on the other hand, have shorter fur that's a little bit smoother.
Another difference is their ears. Llamas have banana-shaped ears that are really big, while alpacas have small, triangular ears.
Finally, let's talk about their size. Llamas are generally bigger than alpacas. They can grow up to 6 feet tall and weigh a lot, while alpacas are usually smaller, only growing up to 3 feet tall.
So, those are some of the main differences between llamas and alpacas. They're both really cool animals, and it's fun to learn about them!With chain-of-thought prompts, you ask the model to explain each step that led to the answer. For example:
Prompt: You are a a very intelligent bot with exceptional critical thinking.
I went to the market and bought 10 apples. I gave 2 apples to your friend and 2 to the helper. I then went and bought 5 more apples and ate 1. How many apples did I remain with?
Let's think step by step.
Answer:
1. You went to the market and bought 10 apples.
2. You gave 2 apples to your friend.
3. You gave 2 apples to the helper.
4. You went back to the market and bought 5 more apples.
5. You ate 1 apple.
Now, let's count how many apples you have:
10 apples (from the first trip to the market) - 2 apples (given to your friend) - 2 apples (given to the helper) = 6 apples
6 apples + 5 apples (from the second trip to the market) = 11 apples
11 apples - 1 apple (that you ate) = 10 apples
Therefore, you remain with 10 apples.
I hope this helps! Do you have any more questions or would you like to play again?Meta shared more examples in section 4.2 of their getting to know Llama notebook.
Availability
The Llama 2 Chat model is available today for all AWS customers in two of the AWS Regions where Bedrock is available: US East (N. Virginia) and US West (Oregon).
You will be charged for model inference. You can choose to be charged on a pay-as-you-go basis, with no upfront or recurring fees; AWS charges per processed input and output tokens. Or you can provision sufficient throughput to meet your application’s performance requirements in exchange for a time-based term commitment. The Bedrock pricing page has the details.
With this information, you’re prepared to start using Amazon Bedrock and the Llama 2 Chat model in your applications.
GitHub Availability Report: October 2023
Post Syndicated from Jakub Oleksy original https://github.blog/2023-11-13-github-availability-report-october-2023/
In October, we experienced two incidents that resulted in degraded performance across GitHub services.
October 17 10:59 UTC (lasting 2 hours and 49 minutes)
From 10:59 UTC to 13:48 UTC on October 17, GitHub Codespaces service was degraded due to an outage in authentication. This issue impacted 67% of users over this time period, with users seeing failures to create and start their Codespaces. The regional authentication layer experienced throttling with a global third-party dependency due to increased load from onboarding a new Codespaces region. The Codespaces team mitigated manually by reducing load on the external dependency. Following the incident, the Codespaces team is actively evaluating and implementing scaling improvements to make the service more resilient to increasing demands. These include implementing regional-level caching to minimize calls to the dependency and incorporating measures to ensure the continued health of the authentication service in the event of errors.
October 25 09:13 UTC (lasting 3 hours and 27 minutes cumulatively)
On October 25 through 26, GitHub Copilot experienced multiple short and partial outages which affected code completions.
GitHub Copilot completions are currently hosted in multiple regions globally. Users are typically routed to the nearest geographic region, but may be routed to other regions when the nearest region is unhealthy. Beginning at 09:13 UTC on October 25, GitHub Copilot began experiencing partial outages of individual regions, lasting approximately 12 minutes per region. These outages were due to the nodes hosting the completion model being upgraded by an automated process, and a subset of GitHub Copilot users experienced completion errors during this timeframe. The issue was fully resolved at 02:40 UTC on October 26.
In order to prevent similar outages from happening in the future, we have taken steps to disable the automated upgrade behavior that we identified as the root cause, as well as prioritizing improvements to our global load balancing during regional outages.
Please follow our status page for real-time updates on status changes. To learn more about what we’re working on, check out the GitHub Engineering Blog.
The post GitHub Availability Report: October 2023 appeared first on The GitHub Blog.
Amazon Bedrock now provides access to Cohere Command Light and Cohere Embed English and multilingual models
Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/amazon-bedrock-now-provides-access-to-cohere-command-light-and-cohere-embed-english-and-multilingual-models/
Cohere provides text generation and representation models powering business applications to generate text, summarize, search, cluster, classify, and utilize Retrieval Augmented Generation (RAG). Today, we’re announcing the availability of Cohere Command Light and Cohere Embed English and multilingual models on Amazon Bedrock. They’re joining the already available Cohere Command model.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies, including AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon, along with a broad set of capabilities to build generative AI applications, simplifying the development while maintaining privacy and security. With this launch, Amazon Bedrock further expands the breadth of model choices to help you build and scale enterprise-ready generative AI. You can read more about Amazon Bedrock in Antje’s post here.
Command is Cohere’s flagship text generation model. It is trained to follow user commands and to be useful in business applications. Embed is a set of models trained to produce high-quality embeddings from text documents.
Embeddings are one of the most fascinating concepts in machine learning (ML). They are central to many applications that process natural language, recommendations, and search algorithms. Given any type of document, text, image, video, or sound, it is possible to transform it into a suite of numbers, known as a vector. Embeddings refer specifically to the technique of representing data as vectors in such a way that it captures meaningful information, semantic relationships, or contextual characteristics. In simple terms, embeddings are useful because the vectors representing similar documents are “close” to each other. In more formal terms, embeddings translate semantic similarity as perceived by humans to proximity in a vector space. Embeddings are typically generated through training algorithms or models.
Cohere Embed is a family of models trained to generate embeddings from text documents. Cohere Embed comes in two forms, an English language model and a multilingual model, both of which are now available in Amazon Bedrock.
There are three main use cases for text embeddings:
Semantic searches – Embeddings enable searching collections of documents by meaning, which leads to search systems that better incorporate context and user intent compared to existing keyword-matching systems.
Text Classification – Build systems that automatically categorize text and take action based on the type. For example, an email filtering system might decide to route one message to sales and escalate another message to tier-two support.
Retrieval Augmented Generation (RAG) – Improve the quality of a large language model (LLM) text generation by augmenting your prompts with data provided in context. The external data used to augment your prompts can come from multiple data sources, such as document repositories, databases, or APIs.
Imagine you have hundreds of documents describing your company policies. Due to the limited size of prompts accepted by LLMs, you have to select relevant parts of these documents to be included as context into prompts. The solution is to transform all your documents into embeddings and store them in a vector database, such as OpenSearch.
When a user wants to query this corpus of documents, you transform the user’s natural language query into a vector and perform a similarity search on the vector database to find the most relevant documents for this query. Then, you embed (pun intended) the original query from the user and the relevant documents surfaced by the vector database together in a prompt for the LLM. Including relevant documents in the context of the prompt helps the LLM generate more accurate and relevant answers.
You can now integrate Cohere Command Light and Embed models in your applications written in any programming language by calling the Bedrock API or using the AWS SDKs or the AWS Command Line Interface (AWS CLI).
Cohere Embed in action
Those of you who regularly read the AWS News Blog know we like to show you the technologies we write about.
We’re launching three distinct models today: Cohere Command Light, Cohere Embed English, and Cohere Embed multilingual. Writing code to invoke Cohere Command Light is no different than for Cohere Command, which is already part of Amazon Bedrock. So for this example, I decided to show you how to write code to interact with Cohere Embed and review how to use the embedding it generates.
To get started with a new model on Bedrock, I first navigate to the AWS Management Console and open the Bedrock page. Then, I select Model access on the bottom left pane. Then I select the Edit button on the top right side, and I enable access to the Cohere model.
Now that I know I can access the model, I open a code editor on my laptop. I assume you have the AWS Command Line Interface (AWS CLI) configured, which will allow the AWS SDK to locate your AWS credentials. I use Python for this demo, but I want to show that Bedrock can be called from any language. I also share a public gist with the same code sample written in the Swift programming language.
Back to Python, I first run the ListFoundationModels API call to discover the modelId for Cohere Embed.
import boto3
import json
import numpy
bedrock = boto3.client(service_name='bedrock', region_name='us-east-1')
listModels = bedrock.list_foundation_models(byProvider='cohere')
print("\n".join(list(map(lambda x: f"{x['modelName']} : { x['modelId'] }", listModels['modelSummaries']))))
Running this code produces the list:
Command : cohere.command-text-v14
Command Light : cohere.command-light-text-v14
Embed English : cohere.embed-english-v3
Embed Multilingual : cohere.embed-multilingual-v3I select cohere.embed-english-v3 model ID and write the code to transform a text document into an embedding.
cohereModelId = 'cohere.embed-english-v3'
# For the list of parameters and their possible values,
# check Cohere's API documentation at https://docs.cohere.com/reference/embed
coherePayload = json.dumps({
'texts': ["This is a test document", "This is another document"],
'input_type': 'search_document',
'truncate': 'NONE'
})
bedrock_runtime = boto3.client(
service_name='bedrock-runtime',
region_name='us-east-1'
)
print("\nInvoking Cohere Embed...")
response = bedrock_runtime.invoke_model(
body=coherePayload,
modelId=cohereModelId,
accept='application/json',
contentType='application/json'
)
body = response.get('body').read().decode('utf-8')
response_body = json.loads(body)
print(np.array(response_body['embeddings']))
The response is printed
[ 1.234375 -0.63671875 -0.28515625 ... 0.38085938 -1.2265625 0.22363281]
Now that I have the embedding, the next step depends on my application. I can store this embedding in a vector store or use it to search similar documents in an existing store, and so on.
To learn more, I highly recommend following the hands-on instructions provided by this section of the Amazon Bedrock workshop. This is an end-to-end example of RAG. It demonstrates how to load documents, generate embeddings, store the embeddings in a vector store, perform a similarity search, and use relevant documents in a prompt sent to an LLM.
Availability
The Cohere Embed models are available today for all AWS customers in two of the AWS Regions where Amazon Bedrock is available: US East (N. Virginia) and US West (Oregon).
AWS charges for model inference. For Command Light, AWS charges per processed input or output token. For Embed models, AWS charges per input tokens. You can choose to be charged on a pay-as-you-go basis, with no upfront or recurring fees. You can also provision sufficient throughput to meet your application’s performance requirements in exchange for a time-based term commitment. The Amazon Bedrock pricing page has the details.
With this information, you’re ready to use text embeddings with Amazon Bedrock and the Cohere Embed models in your applications.
Your guide to AWS Analytics at AWS re:Invent 2023
Post Syndicated from Imtiaz Sayed original https://aws.amazon.com/blogs/big-data/your-guide-to-aws-analytics-at-aws-reinvent-2023/
Join the AWS Analytics team at AWS re:Invent this year, where new ideas and exciting innovations come together.
For those in the data world, this post provides a curated guide for all analytics sessions that you can use to quickly schedule and build your itinerary. Book your spot early for the sessions you do not want to miss. You can do this through the attendee portal, and if you cannot make it in person, get a free pass to watch the live sessions online.
We are raising the bar this year on learning while having fun! Visit us at the AWS Analytics Kiosk in the AWS Village at the Expo to discover the AWS Analytics Superhero in you, participate in a playful quiz and AWS book signing events. Watch this space for additional details.
2023 AWS Analytics Superheroes
We are excited to introduce the 2023 AWS Analytics Superheroes at this year’s re:Invent conference! Are you lightning fast and ultra-adaptable like Wire Weaver? A shapeshifting guardian and protector of data like Data Lynx? Or a digitally clairvoyant master of data insights like Cloud Sight? Join us at the Analytics kiosk to learn more about which AWS Analytics Superhero you are and receive superhero SWAG!
#AWSanalytics #awsfordata #awsreinvent2023
Keynotes
KEY002| Adam Selipsky (CEO, Amazon Web Services) | Tuesday, Nov. 28 | 8:30 AM – 10:30 AM (PDT)
Join Adam Selipsky, CEO of Amazon Web Services, as he shares his perspective on cloud transformation. He highlights innovations in data, infrastructure, and artificial intelligence and machine learning that are helping AWS customers achieve their goals faster, mine untapped potential, and create a better future.
KEY003| Swami Sivasubramanian (Vice President, Data and AI at AWS) | Nov. 29 | 8:30 AM – 10:30 AM (PDT)
A powerful relationship between humans, data, and AI is unfolding right before us. Generative AI is augmenting our productivity and creativity in new ways, while also being fueled by massive amounts of enterprise data and human intelligence. Join Swami Sivasubramanian, Vice President of Data and AI at AWS, to discover how you can use your company data to build differentiated generative AI applications and accelerate productivity for employees across your organization. Also hear from customer speakers with real-world examples of how they’ve used their data to support their generative AI use cases and create new experiences for their customers.
KEY005 | Dr. Werner Vogels (Vice President and Chief Technology Officer, Amazon.com) | Nov. 30 | 8:30 AM – 10:30 AM (PDT)
Join Dr. Werner Vogels, Amazon.com’s VP and CTO, for his twelfth re:Invent appearance. In his keynote, he covers best practices for designing resilient and cost-aware architectures. He also discusses why artificial intelligence is something every builder must consider when developing systems and the impact this will have in our world.
Analytics Innovation Talk
ANT219-INT | G2 Krishnamoorthy | (Vice President of Analytics) | Data drives transformation: Data foundations with AWS analytics | Nov. 30 | 2:00 PM – 3:00 PM (PDT)
Data is the differentiator that drives your current business needs while simultaneously preparing you for the future. As your company transforms, you need a data foundation for business applications, new technical innovations, and data-driven business initiatives. Join G2 Krishnamoorthy, Vice President of AWS Analytics, to discuss strategies for embedding analytics into your applications and ideas for building a data foundation that supports your business initiatives. With new capabilities for self-service and simpler builder experiences, you can democratize data access for line-of-business users, analysts, scientists, and engineers. Hear inspiring stories from adidas, GlobalFoundries, and University of California, Irvine.
Breakout sessions
re:Invent breakout sessions are lecture-style, 1-hour long sessions delivered by AWS experts, customers, and partners.
| Monday, Nov 27 | Tuesday, Nov 28 | Wednesday, Nov 29 | Thursday, Nov 30 |
|
8:30 AM – 9:30 AM (PDT) Wynn ANT321 | How Rocket Companies run their data science platform on AWS |
11:30 AM – 12:30 PM (PDT) Mandalay Bay ANT325 | Amazon Redshift: A decade of innovation in cloud data warehousing |
9:00 AM – 10:00 AM (PDT) MGM Grand ANT319 | Building an open source data strategy on AWS |
12:30 PM – 1:30 PM (PDT) Mandalay Bay ANT326 | Set up a zero-ETL-based analytics architecture for your organizations |
|
10:00 AM – 11:00 AM (PDT) Mandalay Bay SVS307 | Scaling serverless data processing with Amazon Kinesis and Apache Kafka |
12:30 PM – 1:30 PM (PDT) Mandalay Bay ANT202| What’s new in Amazon DataZone |
9:00 AM – 10:00 AM (PDT) Wynn ANT204 | What’s new with Amazon EMR and Amazon Athena |
2:00 PM – 3:00 PM (PDT) Mandalay Bay ANT203 | What’s new in Amazon Redshift |
|
2:30 PM – 3:30 PM (PDT) Mandalay Bay ANT208-S | Build cloud data management across analytics, ML and generative AI applications (sponsored by Informatica) |
2:30 PM – 3:30 PM (PDT) Ceasars Forum ANT322 | Modernize analytics by moving your data warehouse to Amazon Redshift |
10:00 AM – 11:00 AM (PDT) Ceasars Forum ANT209 | Optimizing TCO for business-critical analytics |
2:30 PM – 3:30 PM (PDT) Ceasars Forum ANT210 | Improve your search with vector capabilities in Amazon OpenSearch Service |
|
3:00 PM – 4:00 PM (PDT) MGM Grand COM308 | Serverless data streaming: Amazon Kinesis Data Streams and AWS Lambda |
4:00 PM – 5:00 PM (PDT) Ceasars Forum ANT329 | Best practices for analytics and generative AI on AWS |
10:30 AM – 11:30 AM (PDT) Wynn ANT324 | Easily and securely prepare, share, and query data |
3:30 PM – 4:30 PM (PDT) Mandalay Bay ANT220 | What’s new with AWS data integration |
|
3:00 PM – 4:00 PM (PDT) Wynn ANT320 | How Electronic Arts modernized its data platform with Amazon EMR |
4:00 PM – 5:00 PM (PDT) Mandalay Bay ANT317 | How Rivian builds real-time analytics from electric vehicles |
10:30 AM – 11:30 AM (PDT) MGM Grand ANT303 | What’s new in AWS Lake Formation |
4:00 PM – 5:00 PM (PDT) Caesars Forum ANT301 | What’s new in Amazon OpenSearch Service |
|
3:00 PM – 4:00 PM (PDT) Mandalay Bay BSI203 | Enhance your applications with Amazon QuickSight embedded analytics |
. |
11:30 AM – 12:30 PM (PDT) Ceasars Forum ANT318 | Accelerate innovation with end-to-end serverless data architecture |
. |
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4:30 PM – 5:30 PM (PDT) Wynn ANT207 | Understand your data with business context |
. |
1:00 PM – 2:00 PM (PDT) Venetian ANT201 | Accelerate innovation with real-time data |
. |
|
5:30 PM – 6:30 PM (PDT) MGM Grand ANT350 | Security analytics and observability with Amazon OpenSearch Service |
. |
1:00 PM – 2:00 PM (PDT) Ceasars Forum BSI101 | Generative BI in Amazon QuickSight |
. |
| . | . |
2:30 PM – 3:30 PM (PDT) Ceasars Forum BSI205 | What’s new with Amazon QuickSight |
. |
| . | . |
5:30 PM – 6:30 PM (PDT) Mandalay Bay ANT205 | Curate your data at scale |
. |
| . | . |
5:30 PM – 6:30 PM (PDT) Mandalay Bay ANT323 | Smarter, faster analytics with generative AI and ML |
. |
Chalk talks
Chalk talks are an hour-long, highly interactive content format with a small audience. Each begins with a short lecture delivered by an AWS expert, followed by a Q&A session with the audience.
| Monday, Nov 27 | Tuesday, Nov 28 | Wednesday, Nov 29 | Thursday, Nov 30 |
|
10:00 AM – 11:00 AM (PDT) Wynn OPN311 | Extending OpenSearch |
11:00 AM – 12:00 PM (PDT) Wynn ANT336-R | Governed data sharing with Amazon DataZone and AWS Lake Formation [REPEAT] |
10:00 AM – 11:00 AM (PDT) Ceasars Forum ANT316 | Fast-track streaming ETL with AWS streaming data services |
11:00 AM – 12:00 PM (PDT) Wynn ANT347 | Using natural language to author data integration applications |
|
11:30 AM – 12:30 PM (PDT) Wynn OPN308-R | Build and operate a Zero Trust Apache Kafka cluster [REPEAT] |
11:00 AM – 12:00 PM (PDT) Mandalay Bay ANT338-R | Migrate legacy ETL to AWS Glue [REPEAT] |
10:00 AM – 11:00 AM (PDT) Wynn ANT339 | Optimizing Apache Spark workloads with Amazon EMR Serverless |
12:30 PM – 1:30 PM (PDT) Mandalay Bay ANT314 | Migrating self-managed Apache Flink to fully managed on AWS |
|
2:30 PM – 3:30 PM (PDT) Mandalay Bay ANT333-R | Data integration with AWS Glue and Amazon MWAA [REPEAT] |
11:30 AM – 12:30 PM (PDT) Ceasars Forum ANT328 | Accessing open table formats for superior data lake analytics |
10:30 AM – 11:30 AM (PDT) Ceasars Forum STG328 | Powering your data lakes and analytics with Amazon S3 |
2:30 PM – 3:30 PM (PDT) Mandalay Bay ANT341 | Reduce downtime and optimize costs through data pipeline observability |
|
4:00 PM – 5:00 PM (PDT) MGM Grand ANT315 | Accelerating value from data: Migrate from batch to stream processing |
12:30 PM – 1:30 PM (PDT) MGM Grand BSI302 | Architecting governed BI for all users with Amazon QuickSight |
11:30 AM – 12:30 PM (PDT) Ceasars Forum ANT340 | Powering observability with AI and Amazon OpenSearch Ingestion |
2:30 PM – 3:30 PM (PDT) Ceasars Forum ANT342-R | Scaling analytics with data lakes and data warehouses [REPEAT] |
|
4:00 PM – 5:00 PM (PDT) Mandalay Bay ANT311-R | Stream data and build transactional lakes with AWS Glue streaming [REPEAT] |
12:30 PM – 1:30 PM (PDT) Wynn ANT403 | How to migrate your Apache Kafka workloads to Amazon MSK |
2:30 PM – 3:30 PM (PDT) Ceasars Forum ANT346-R | Understanding zero-ETL with AWS analytics [REPEAT] |
3:30 PM – 4:30 PM (PDT) MGM Grand STG336-R | Scale data lake access control and enhance governance with Amazon S3 [REPEAT] |
|
4:30 PM – 5:30 PM (PDT) Wynn ANT337 | Identify and remediate security threats with Amazon OpenSearch Service |
1:00 PM – 2:00 PM (PDT) Ceasars Forum ANT327-R | 5 steps to successfully migrating to Amazon OpenSearch Service [REPEAT] |
2:30 PM – 3:30 PM (PDT) Wynn ANT334 | End-to-end data and machine learning governance on AWS |
3:30 PM – 4:30 PM (PDT) Ceasars Forum ANT344 | Self-service analytics for all |
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.4:30 PM – 5:30 PM (PDT) Mandalay Bay BSI301-R | Architectural patterns for embedded analytics using Amazon QuickSight [REPEAT] |
2:00 PM – 3:00 PM (PDT) MGM Grand BSI401 | DevOps strategies for Amazon QuickSight business intelligence assets |
4:00 PM – 5:00 PM (PDT) Ceasars Forum ANT343 | Securely manage data across organizational boundaries |
4:00 PM – 5:00 PM (PDT) MGM Grand TRV303 | Build a digital concierge for travelers and guests with generative AI |
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2:30 PM – 3:30 PM (PDT) Mandalay Bay ANT335 | Get the most out of your data warehousing workloads |
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5:30 PM – 6:30 PM (PDT) Ceasars Forum ANT349-R | Advanced real-time analytics and ML in your data warehouse [REPEAT] |
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Builders’ sessions
These are 1-hour small-group sessions with up to nine attendees per table and one AWS expert. Each builders’ session begins with a short explanation or demonstration of what you’re going to build. When the demonstration is complete, bring your laptop to experiment and build with the AWS expert.
| Monday, Nov 27 | Tuesday, Nov 28 | Wednesday, Nov 29 |
|
2:30 PM – 3:30 PM (PDT) Mandalay Bay ANT313-R | Use data catalogs to improve self-service analytics [REPEAT] |
11:00 AM – 12:00 PM (PDT) Mandalay Bay OPN401 | Apache Hudi on AWS: Tuning for cost and performance |
4:00 PM – 5:00 PM (PDT) Ceasars Forum ANT308-R | Build large-scale transactional data lakes with Apache Iceberg on AWS [REPEAT] |
| . |
11:00 AM – 12:00 PM (PDT) MGM Grand BSI206 | Scale enterprise BI securely with Amazon QuickSight |
. |
Workshops
Workshops are 2-hour interactive sessions where you work in teams or individually to solve problems using AWS services. Each workshop starts with a short lecture, and the rest of the time is spent working the problem. Bring your laptop to build along with AWS experts.
| Monday, Nov 27 | Tuesday, Nov 28 | Wednesday, Nov 29 |
|
2:00 PM – 4:00 PM (PDT) Mandalay Bay BSI201 | Build dashboards, reports, and explore generative BI in Amazon QuickSight |
11:00 AM – 1:00 PM (PDT) MGM Grand ANT306 | Build a data foundation to power your generative AI applications |
11:30 AM – 1:30 PM (PDT) Ceasars Forum ANT312 | Using Amazon OpenSearch Service as a vector database for gen AI apps |
|
2:30 PM – 4:30 PM (PDT) Wynn ANT305-R | Log analytics made easy with Amazon OpenSearch Serverless [REPEAT] |
11:30 AM – 1:30 PM (PDT) Mandalay Bay ANT401-R | Event detection with Amazon MSK and Amazon Managed Service for Apache Flink [REPEAT] |
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11:30 AM – 1:30 PM (PDT) MGM Grand BSI202 | Quickly build predictive dashboards using no-code ML and generative BI |
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2:00 PM – 4:00 PM (PDT) Wynn ANT310 | Share data across Regions and organizations for near-real-time insights |
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11:30 AM – 1:30 PM (PDT) Ceasars Forum ANT304-R | A pragmatic approach to data governance on AWS [REPEAT] |
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11:30 AM – 1:30 PM (PDT) Venetian AIM350 | Personalized marketing content with generative AI and Amazon Personalize |
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12:00 PM – 2:00 PM (PDT) Ceasars Forum ANT402 | Protect and securely share the right data |
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12:00 PM – 2:00 PM (PDT) Wynn ANT307 | Connect and analyze all your data with zero-ETL approaches |
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Code talks
Code talks are similar to our popular chalk talk format, but instead of focusing on an architecture solution with whiteboarding, the speakers lead an interactive discussion featuring live coding or code samples. These sessions focus on the actual code that goes into building a solution. Attendees will learn the “why” behind the solution and see it come to life, and even the errors that are bound to happen. Attendees are encouraged to ask questions and follow along.
| Monday, Nov 27 | Tuesday, Nov 28 | Wednesday, Nov 29 | Thursday, Nov 30 |
|
11:30 AM – 12:30 PM (PDT) Wynn ANT332-R | Customize Amazon Athena to integrate with new data sources [REPEAT] |
1:00 PM – 2:00 PM (PDT) Wynn ANT345 | Simplify working with data across multicloud with AWS analytics |
5:30 PM – 6:30 PM (PDT) Wynn ANT405 | Build a Flink application on Amazon Managed Service for Apache Flink |
4:00 PM – 5:00 PM (PDT) Wynn AIM366 | Data preparation for ML at scale with Amazon SageMaker notebooks |
Conclusion
We hope this post acts as your go-to resource for navigating the AWS analytics track at re:Invent 2023. For staying in the know about the most recent trends and advancements in AWS Analytics, follow our dedicated LinkedIn page. Visit the Amazon QuickSight guide to learn what’s new in Business Intelligence.
About the authors
Imtiaz (Taz) Sayed is the WW Tech Leader for Analytics at AWS. He enjoys engaging with the community on all things data and analytics. He can be reached via LinkedIn.
Navnit Shukla serves as an AWS Specialist Solution Architect with a focus on Analytics. He possesses a strong enthusiasm for assisting clients in discovering valuable insights from their data. Through his expertise, he constructs innovative solutions that empower businesses to arrive at informed, data-driven choices. Notably, Navnit Shukla is the accomplished author of the book titled “Data Wrangling on AWS.” He can be reached via LinkedIn.
How GamesKraft uses Amazon Redshift data sharing to support growing analytics workloads
Post Syndicated from Anshuman Varshney original https://aws.amazon.com/blogs/big-data/how-gameskraft-uses-amazon-redshift-data-sharing-to-support-growing-analytics-workloads/
This post is co-written by Anshuman Varshney, Technical Lead at Gameskraft.
Gameskraft is one of India’s leading online gaming companies, offering gaming experiences across a variety of categories such as rummy, ludo, poker, and many more under the brands RummyCulture, Ludo Culture, Pocket52, and Playship. Gameskraft holds the Guinness World Record for organizing the world’s largest online rummy tournament, and is one of India’s first gaming companies to build an ISO certified platform.
Amazon Redshift is a fully managed data warehousing service that offers both provisioned and serverless options, making it more efficient to run and scale analytics without having to manage your data warehouse. Amazon Redshift enables you to use SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and machine learning (ML) to deliver the best price-performance at scale.
In this post, we show how Gameskraft used Amazon Redshift data sharing along with concurrency scaling and WLM optimization to support its growing analytics workloads.
Amazon Redshift use case
Gameskraft used Amazon Redshift RA3 instances with Redshift Managed Storage (RMS) for their data warehouse. The upstream data pipeline is a robust system that integrates various data sources, including Amazon Kinesis and Amazon Managed Streaming for Apache Kafka (Amazon MSK) for handling clickstream events, Amazon Relational Database Service (Amazon RDS) for delta transactions, and Amazon DynamoDB for delta game-related information. Additionally, data is extracted from vendor APIs that includes data related to product, marketing, and customer experience. All of this diverse data is then consolidated into the Amazon Simple Storage Service (Amazon S3) data lake before being uploaded to the Redshift data warehouse. These upstream data sources constitute the data producer components.
Gameskraft used Amazon Redshift workload management (WLM) to manage priorities within workloads, with higher priority being assigned to the extract, transform, and load (ETL) queue that runs critical jobs for data producers. The downstream consumers consist of business intelligence (BI) tools, with multiple data science and data analytics teams having their own WLM queues with appropriate priority values.
As Gameskraft’s portfolio of gaming products increased, it led to an approximate five-times growth of dedicated data analytics and data science teams. Consequently, there was a fivefold rise in data integrations and a fivefold increase in ad hoc queries submitted to the Redshift cluster. These query patterns and concurrency were unpredictable in nature. Also, over time the number of BI dashboards (both scheduled and live) increased, which contributed to more queries being submitted to the Redshift cluster.
With this growing workload, Gameskraft was observing the following challenges:
- Increase in critical ETL job runtime
- Increase in query wait time in multiple queues
- Impact of unpredictable ad hoc query workloads across other queues in the cluster
Gameskraft was looking for a solution that would help them mitigate all these challenges, and provide flexibility to scale ingestion and consumption workload processing independently. Gameskraft was also looking for a solution that would cater to their unpredictable future growth.
Solution overview
Gameskraft tackled these challenges in a phased manner using Amazon Redshift concurrency scaling, Amazon Redshift data sharing, Amazon Redshift Serverless, and Redshift provisioned clusters.
Amazon Redshift concurrency scaling lets you easily support thousands of concurrent users and concurrent queries, with consistently fast query performance. As concurrency increases, Amazon Redshift automatically adds query processing power in seconds to process queries without any delays. When the workload demand subsides, this extra processing power is automatically removed, so you pay only for the time when concurrency scaling clusters are in use. Amazon Redshift offers 1 hour of free concurrency scaling credits per active cluster per day, allowing you to accumulate 30 hours of free credits per month.
Gameskraft enabled concurrency scaling in selective WLM queues to ease the query wait time in those queues during peak usage and also helped reduce ETL query runtime. In the prior setup, we maintained four specialized queues for ETL, ad hoc queries, BI tools, and data science. To prevent blockages for other processes, we imposed minimal query timeouts using query monitoring rules (QMR). However, both the ETL and BI tools queues were persistently occupied, impacting the performance of the remaining queues.
Concurrency scaling helped alleviate query wait time in the ad hoc query queue. Still, the challenge of downstream consumption workloads (like ad hoc queries) impacting ingestion persisted, and Gameskraft was looking for a solution to manage these workloads independently.
The following table summarizes the workload management configuration prior to the solution implementation.
| Queue | Usage | Concurrency Scaling Mode | Concurrency on Main / Memory % | Query Monitoring Rules |
etl |
For ingestion from multiple data integration | off | auto | Stop action on: Query runtime (seconds) > 2700 |
report |
For scheduled reporting purposes | off | auto | Stop action on: Query runtime (seconds) > 600 |
datascience |
For data science workloads | off | auto | Stop action on: Query runtime (seconds) > 300 |
readonly |
For ad hoc and day-to-day analysis | auto | auto | Stop action on: Query runtime (seconds) > 120 |
bi_tool |
For BI tools | auto | auto | Stop action on: Query runtime (seconds) > 300 |
To achieve flexibility in scaling, Gameskraft used Amazon Redshift data sharing. Amazon Redshift data sharing allows you to extend the ease of use, performance, and cost benefits offered by a single cluster to multi-cluster deployments while being able to share data. Data sharing enables instant, granular, and fast data access across Amazon Redshift data warehouses without the need to copy or move it. Data sharing provides live access to data so that users always observe the most up-to-date and consistent information as it’s updated in the data warehouse. You can securely share live data across provisioned clusters, serverless endpoints within AWS account, across AWS accounts, and across AWS Regions.
Data sharing builds on Redshift Managed Storage (RMS), which underpins RA3 provisioned clusters and serverless workgroups, allowing multiple warehouses to query the same data with separate isolated compute. Queries accessing shared data run on the consumer cluster and read data from RMS directly without impacting the performance of the producer cluster. You can now rapidly onboard workloads with diverse data access patterns and SLA requirements and not be concerned about resource contention.
We chose to run all ETL workloads in the primary producer cluster to manage ETL independently. We used data sharing to share read-only access to data with a data science serverless workgroup, a BI provisioned cluster, an ad hoc query provisioned cluster, and a data integration serverless workgroup. Teams using these separate compute resources could then query the same data without copying the data between the producer and consumer. Additionally, we introduced concurrency scaling to the consumer queues, prioritizing BI tools, and extended the timeout for the remaining queues. These modifications notably enhanced overall efficiency and throughput.
The following table summarizes the new workload management configuration for the producer cluster.
| Queue | Usage | Concurrency Scaling Mode | Concurrency on Main / Memory % | Query Monitoring Rules |
etl |
For ingestion from multiple data integration | auto | auto | Stop action on: Query runtime (seconds) > 3600 |
The following table summarizes the new workload management configuration for the consumer cluster.
| Queue | Usage | Concurrency Scaling Mode | Concurrency on Main / Memory % | Query Monitoring Rules |
report |
For scheduled reporting purposes | off | auto | Stop action on: Query runtime (seconds) > 1200 Query queue time (seconds) > 1800 Spectrum scan row count (rows) > 100000 Spectrum scan (MB) > 3072 |
datascience |
For data science workloads | off | auto | Stop action on: Query runtime (seconds) > 600 Query queue time (seconds) > 1800 Spectrum scan row count (rows) > 100000 Spectrum scan (MB) > 3072 |
readonly |
For ad hoc and day-to-day analysis | auto | auto | Stop action on: Query runtime (seconds) > 900 Query queue time (seconds) > 3600 Spectrum scan (MB) > 3072 Spectrum scan row count (rows) > 100000 |
bi_tool_live |
For live BI tools | auto | auto | Stop action on: Query runtime (seconds) > 900 Query queue time (seconds) > 1800 Spectrum scan (MB) > 1024 Spectrum scan row count (rows) > 1000 |
bi_tool_schedule |
For scheduled BI tools | auto | auto | Stop action on: Query runtime (seconds) > 1800 Query queue time (seconds) > 3600 Spectrum scan (MB) > 1024 Spectrum scan row count (rows) > 1000 |
Solution implementation
Gameskraft is dedicated to maintaining uninterrupted system operations, prioritizing seamless solutions over downtime. In pursuit of this principle, strategic measures were undertaken to ensure a smooth migration process towards enabling data sharing, which included the following steps:
- Planning:
- Replicating users and groups to the consumer, to mitigate potential access complications for analytics, data science, and BI teams.
- Establishing a comprehensive setup within the consumers, encompassing essential components like external schemas for Amazon Redshift Spectrum.
- Fine-tuning WLM configurations tailored to the consumer’s requirements.
- Implementation:
- Introducing insightful monitoring dashboards in Grafana for CPU utilization, read/write throughputs, IOPS, and latencies specific to the consumer cluster, enhancing oversight capabilities.
- Changing all interleaved key tables on the producer cluster to compound sortkey tables to seamlessly transition data.
- Creating an external schema from the data share database on the consumer, mirroring that of the producer cluster with identical names. This approach minimizes the need for making query adjustments in multiple locations.
- Testing:
- Conducting an internal week-long regression testing and auditing process to meticulously validate all data points by running the same workload and twice the workload.
- Final changes:
- Updating the DNS record for the cluster endpoint, which included replacing the consumer cluster’s endpoint to the same domain as the producer cluster’s endpoint, to streamline connections and avoid making changes in multiple places.
- Ensuring data security and access control by revoking group and user privileges from the producer cluster.
The following diagram illustrates the Gameskraft Amazon Redshift data sharing architecture.
The following diagram illustrates the Amazon Redshift data sharing architecture with multiple consumer clusters.
With data sharing implementation, Gameskraft was able to isolate the producer and consumer workloads. Data sharing also provided the flexibility to independently scale the producer and consumer data warehouses.
The implementation of the overall solution helped Gameskraft support more frequent data refresh (43% reduction in overall job runtime) for its ETL workload, which runs on the producer cluster, along with capabilities to support a growing (five-times increase in users, BI workloads, and ad hoc queries) and unpredictable consumer workload.
The following dashboards show some of the critical ETL pipeline runtimes (before solution implementation and after solution implementation).
The first shows the delta P1/P2/P3 job runs before and after solution implementation (duration in minutes).
The following shows the daily event ETL P1/P2/P3 job runs before and after solution implementation (duration in minutes).
Key considerations
Gameskraft embraces a modern data architecture, with the data lake residing in Amazon S3. To grant seamless access to the data lake, we use the innovative capabilities of Redshift Spectrum, which is a bridge between the data warehouse (Amazon Redshift) and data lake (Amazon S3). It allows you to perform data transformations and analysis directly on data stored in Amazon S3, without having to duplicate the data into your Redshift cluster.
Gameskraft had a couple of key learnings while implementing this data sharing solution:
- First, as of this writing, Amazon Redshift data sharing doesn’t support adding external schemas, tables, or late-binding views on external tables to the data share. To enable this, we have created an external schema as a pointer to AWS Glue database. The same AWS Glue database is referenced in the external schema on the consumer side.
- Second, Amazon Redshift doesn’t support sharing tables with interleaved sort keys and views that refer to tables with interleaved sort keys. Due to the presence of interleaved sort keys across numerous tables and views, a prerequisite for inclusion into the data share involves revising the sort key configuration to use compound sort key.
Conclusion
In this post, we saw how Gameskraft used data sharing and concurrency scaling in Amazon Redshift with a producer and consumer cluster architecture to achieve the following:
- Reduce query wait time for all queues in the producer and consumer
- Scale the producer and consumer independently based on workload and queue requirements
- Improve ETL pipeline performance and the data refresh cycle to support more frequent refreshes in the producer cluster
- Onboard more queues and workloads (BI tools queue, data integration queue, data science queue, downstream team’s queue, ad hoc query queue) in the consumer without impacting the ETL pipeline in the producer cluster
- Flexibility to use multiple consumers with a mix of provisioned Redshift cluster and Redshift Serverless
These Amazon Redshift features and architecture can help support a growing and unpredictable analytics workload.
About the Authors
Anshuman Varshney is Technical Lead at Gameskraft with a background in both backend and data engineering. He has a proven track record of leading and mentoring cross-functional teams to deliver high-performance, scalable solutions. Apart from work, he relishes moments with his family, indulges in cinematic experiences, and seizes every opportunity to explore new destinations through travel.
Prafulla Wani is an Amazon Redshift Specialist Solution Architect at AWS. He works with AWS customers on analytics architecture designs and Amazon Redshift proofs of concept. In his spare time, he plays chess with his son.
Saurov Nandy is a Solutions Architect at AWS. He works with AWS customers to design and implement solutions that solve complex business problems. In his spare time, he likes to explore new places and indulge in photography and video editing.
Shashank Tewari is a Senior Technical Account Manager at AWS. He helps AWS customers optimize their architectures to achieve performance, scale, and cost efficiencies. In his spare time, he likes to play video games with his kids. During vacations, he likes to trek on mountains and take up adventure sports.
AWS Weekly Roundup – CloudFront security dashboard, EBS snapshots improvements, and more – November 13, 2023
Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-cloudfront-security-dashboard-ebs-snapshots-improvements-and-more-november-13-2023/
This week, it was really difficult to choose what to recap here because, as we’re getting closer to AWS re:Invent, service teams are delivering new capabilities at an incredible pace.
Last week’s launches
Here are some of the launches that caught my attention last week:
Amazon Aurora – Aurora MySQL zero-ETL integration with Amazon Redshift is now generally available. Get a walk-through in our AWS News Blog post. Here’s a recap of data integration innovations at AWS. Optimized reads for Aurora PostgreSQL provide up to 8x improved query latency and up to 30 percent cost savings for I/O-intensive applications. Here’s more of a deep dive from the AWS Database Blog.
Amazon EBS – You can now block public sharing of EBS snapshots. Read more about how that works in the launch post.
Amazon Data Lifecycle Manager – Support for pre- and post-script automation of EBS snapshots simplifies application-consistent snapshots. Here’s how to use it with Windows applications.
AWS Health – There’s now improved visibility into planned lifecycle events like end of standard support of a Kubernetes version in Amazon EKS, Amazon RDS certificate rotations, and end of support for other open source software. Here’s how it works.
Amazon CloudFront – Unified security dashboard to enable, monitor, and manage common security protections for your web applications directly from the CloudFront console. Read more at Introducing CloudFront Security Dashboard, a Unified CDN and Security Experience.
Amazon Connect – Reduced outbound telephony pricing across Europe and South America. It’s also easier now to deliver persistent chat experiences for end users.
AWS Lambda – Busy week for the Lambda team! There is now support for Amazon Linux 2023 as both a managed runtime and a container base image. More details in this Compute Blog post. There’s also enhanced auto scaling for Kafka event sources (the Compute Blog has a post with more details) and faster polling scale-up rate for Amazon SQS events when AWS Lambda functions are configured with SQS.
AWS CodeBuild – Now supports AWS Lambda compute to build and test software packages. Read about how it works in this post.
Amazon SQS – Now supports JSON protocol to reduce latency and client-side CPU usage. More in the launch post. There’s also a new integration for Amazon SQS in the Amazon EventBridge Pipes console (the week before that, Amazon Kinesis Data Streams was also integrated into the EventBridge Pipes console).
Amazon SNS – FIFO topics now support 3,000 messages per second by default.
Amazon EventBridge – There are 22 additional Amazon CloudWatch metrics to help you monitor the performance of your event buses. More info in this post from the AWS Compute Blog.
Amazon OpenSearch Service – Neural search makes it easier to create and manage semantic search applications.
Amazon Timestream – The UNLOAD statement simplifies exporting time-series data for additional insights.
Amazon Comprehend – New trust and safety features with toxicity detection and prompt safety classification. Read how to apply that to generative AI applications using LangChain.
AWS App Runner – Now available in London, Mumbai, and Paris AWS Regions.
AWS Application Migration Service – Support for AWS App2Container replatforming of .NET and Java based applications.
Amazon FSx for OpenZFS – Now available in ten additional AWS Regions with support for additional deployment types in seven Regions.
AWS Global Accelerator – There’s now IPv6 support for Network Load Balancer (NLB) endpoints. It was already available for Application Load Balancers (ALBs) and Amazon Elastic Compute Cloud (Amazon EC2) instances.
Amazon GuardDuty – New machine learning (ML) capability enhances threat detection for Amazon EKS.
Other AWS news
Some other news and blog posts that you might have missed:
AWS Local Zones Credit Program – If you have low-latency or data residency requirements for your application, our Local Zones Credit Program can get you started. Fill out our form to receive $500 in AWS credits and apply it to a Local Zones workload.
Amazon CodeWhisperer – Customizing coding companions for organizations and optimizing for sustainability.
Sharing what we have learned – Creating a correction of errors document to understand what went wrong and what would be done to prevent it from happening again.
Good tips for containers – Securing API endpoints using Amazon API Gateway and Amazon VPC Lattice.
Another post in this amazing series – Let’s Architect! Tools for developers.
A few highlights from Community.AWS:
- From MVC to Modern Web Frameworks
- Reduce Stress and Get Your Fridays Back with Observability and OpenTelemetry
- Sustainable Software Development Life Cycle (S-SDLC)
Don’t miss the latest AWS open source newsletter by my colleague Ricardo.
Upcoming AWS events
Check your calendars and sign up for these AWS events:
AWS Community Days – Join a community-led conference run by AWS user group leaders in your region: Uruguay (November 14), Central Asia (Kazakhstan, Uzbekistan, Kyrgyzstan, and Mongolia on November 17–18), and Guatemala (November 18).
AWS re:Invent (November 27 – December 1) – Join us to hear the latest from AWS, learn from experts, and connect with the global cloud community. Browse the session catalog and attendee guides and check out the highlights for generative AI. In the AWS re:Invent Builder Hub you can find developer-focused sessions, events, competitions, and content.
Here you can browse all upcoming AWS-led in-person and virtual events and developer-focused events.
And that’s all from me for this week. We’re now taking a break. The next weekly roundup will be after re:Invent!
— Danilo
This post is part of our Weekly Roundup series. Check back for a quick roundup of interesting news and announcements from AWS!