Very interesting announcement from OpenAI this morning. We’re excited to make OpenAI's models available directly to customers on Bedrock in the coming weeks, alongside the upcoming Stateful Runtime Environment. With this, builders will have even more choice to pick the right model for the right job. More details at our AWS event in San Francisco tomorrow.

What if your AI could actually remember you? Most AI is stateless, it treats every conversation like meeting you for the first time. AWS and OpenAI are co-creating a Stateful Runtime Environment powered by OpenAI models, available on Amazon Bedrock. @shaown, VP, AWS Technology breaks down the difference between stateful and stateless: go.aws/4vjGz8m

Excited to share the @awscloud #AWSreInvent Architecture Innovation Talk . Watch now to hear how agentic AI is transforming cloud-native application architecture, unlocking faster innovation cycles with the CEOs of @stedi @patternhq @QAD_Community #ai #aws youtu.be/D0UkoghAVM0?si=r_fz…

We’re here! Excited to be at the 14th #AWSreInvent with our customers, partners, and builders. @shaown and I are looking forward to seeing everyone. @awscloud #letsgo #innovation

We asked @awscloud Director Shaown Nandi about his short-term AI chip vision. His answer:

🚨 Huge news: @OpenAI's open weight models are available today on AWS via Amazon Bedrock and Amazon SageMaker AI. This means more access to powerful AI tech and advanced reasoning capabilities for millions of @awscloud customers worldwide. More options, more innovation, more possibilities. 💡🤝

We’ve arrived! @shaown and I are thrilled to share some of the latest announcements at #AWSreInvent 2024. See you in Vegas! Join his leadership session on Tuesday! #aws #amazon

One of the most tedious (but critical tasks) for software development teams is updating foundational software. It’s not new feature work, and it doesn’t feel like you’re moving the experience forward. As a result, this work is either dreaded or put off for more exciting work—or both. Amazon Q, our GenAI assistant for software development, is trying to bring some light to this heaviness. We have a new code transformation capability, and here’s what we found when we integrated it into our internal systems and applied it to our needed Java upgrades: - The average time to upgrade an application to Java 17 plummeted from what’s typically 50 developer-days to just a few hours. We estimate this has saved us the equivalent of 4,500 developer-years of work (yes, that number is crazy but, real). - In under six months, we've been able to upgrade more than 50% of our production Java systems to modernized Java versions at a fraction of the usual time and effort. And, our developers shipped 79% of the auto-generated code reviews without any additional changes. - The benefits go beyond how much effort we’ve saved developers. The upgrades have enhanced security and reduced infrastructure costs, providing an estimated $260M in annualized efficiency gains. This is a great example of how large-scale enterprises can gain significant efficiencies in foundational software hygiene work by leveraging Amazon Q. It’s been a game changer for us, and not only do our Amazon teams plan to use this transformation capability more, but our Q team plans to add more transformations for developers to leverage.

When we started AWS, we looked at the undesirable situation of developers spending 70% of their time on the undifferentiated muck of managing infrastructure (vs only 30% on what differentiated their customer experiences) and aimed to flip that equation on its head. It’s made a pretty big difference in innovation and developer productivity. Roughly 18 years later, we’re trying to solve another 70-30-like misalignment for developers, where developers are telling us that they’re spending roughly 70% of their time on repetitive and tedious tasks and code. Today, we’re launching the general availability of Amazon Q, the world’s most capable GenAI-powered assistant for accelerating software development and leveraging companies’ internal data—and that aims to remove much of the muck of repetitive and tedious coding and data tasks for developers and employees at large. On the software development side, Q doesn’t just generate code, it also tests code, debugs coding conflicts, and transforms code from one form to another (today, developers can save months using Q to move from older versions of Java to newer, more secure and capable ones; in the near future, Q will help developers transform their .net code as well). Q Agents does multi-step planning and reasoning to allow developers to string together multiple requests and have Q implement them. On the internal data side, most companies have voluminous troves of internally relevant data that resides in wikis, intranet pages, Salesforce, storage repositories like Amazon S3, and a bevy of other data stores and SaaS apps that are hard to access. It makes answering straightforward questions about company policies, products, business results, code, people, and many other topics hard and frustrating. Q makes this much simpler. You can point Q at all of your enterprise data repositories, and it’ll search all this data, summarize logically, analyze trends, and engage in dialog with customers about it. We also introduced today a powerful new capability called Q Apps, which lets employees describe, in natural language, what apps they want to build on top of this internal data, and Q Apps will quickly generate that app. This is going to make it *so* much easier for internal teams to build useful apps from their own data. Customers are gravitating to Q, and we already see companies like Brightcove, British Telecom, Datadog, GitLab, GoDaddy, National Australia Bank, NCS, Netsmart, Slalom, Smartsheet, Sun Life, Tata Consultancy Services, Toyota, and Wiz using Q. And, we’ve only been in beta til today. Very excited about how Q will change what’s possible for our customers, and being a part of helping them innovate more quickly. aboutamazon.com/news/aws/ama…

#HereAtAWS we’ve always got exciting innovations in the works, and today we’re announcing a host of new capabilities for Amazon Bedrock, making it easier and more affordable than ever for customers to build advanced #GenAI apps and experiences. go.aws/4a8FNPP

AWS continues to add capabilities to Amazon Bedrock that make it the easiest way to build high quality, low latency, and cost-effective GenAI applications. Today the team announced: - Custom Model Import, which lets customers easily bring their own models (from SageMaker and S3) to Bedrock to leverage its leading GenAI features, such as RAG, agents, Knowledge Bases (guardrails), inference deployment, and advanced customization to keep teaching and refining models. - Model Evaluation, which lets customers quickly select predefined evaluation criteria (like accuracy and robustness) and run reports that help them understand which is the best model for them. - Meta’s new Llama 3 models, now available on Bedrock and can support a wide range of use cases including text summarization and classification, sentiment analysis, language translation, and code generation. - New Cohere Models, Command R and R , are state-of-the-art FMs customers can use to build enterprise-grade generative AI applications with advanced RAG capabilities, in 10 languages, to support their global business operations. These will arrive on Bedrock in the coming weeks. - New Amazon Titan models with Amazon Titan Image Generator (GA today) helping customers produce studio-quality images, at low cost, using natural language prompts (and applies an invisible watermark to help reduce the spread of disinformation)… along with Amazon Titan Text Embeddings V2 model, which reduces storage and compute costs, while out-performing other leading models on text embeddings. Tens of thousands of customers are already building real-world production-ready GenAI apps and we're excited for even more customers to get started. aboutamazon.com/news/aws/ama…

Today, we're announcing major new updates for Amazon Bedrock that give customers unparalleled flexibility, choice, and security when developing advanced generative AI apps. One I’m especially excited about is the Custom Model Import capability, which allows customers to bring their own models to Amazon Bedrock's secure, managed environment. This promises to unlock new possibilities across industries, as customers can seamlessly access their custom model, like any other on Bedrock, and get all the same powerful capabilities—without having to manage the underlying infrastructure themselves. A couple other updates to share: 🔵 Our model evaluation capability is now generally available, making it simple to analyze, compare, and select the best foundation model for your use case. 🔵 Also available today, Guardrails for Amazon Bedrock—which offers industry-leading safety protection, helping customers block harmful content, profanity, personal information and more, out-performing all other services available today. 🔵Finally, Amazon Bedrock already offers the widest choice of fully managed FMs, and is expanding even further with Amazon Titan Text Embeddings V2, the GA of Titan Image Generator, and new models from @Meta, with @cohere's latest models coming soon. Tens of thousands of customers are innovating quickly with Amazon Bedrock. Two such examples: @United Airlines modernized its Passenger Service System, making customer support more efficient. The @NYSE is using Bedrock across several use cases, including the processing of thousands of pages of regulations to provide answers in easy-to-understand language. We know there will be many more success stories like these as we continue to expand Amazon Bedrock, and make it the easiest and fastest way to build and scale secure GenAI applications. aws.amazon.com/blogs/machine…

Next stop London. Who’s ready! Come enjoy an action-packed day at @awscloud Summit London 2024, where the latest in cloud innovation comes to life. #letsgo aws.amazon.com/events/summit…

Fresh out of #AWS Summit Paris: Mistral Large is now publicly available on #AmazonBedrock! bit.ly/3U2UoaC

Proud to be the 29,148th backer ⛈️ on @BackerKit Crowdfunding for Words of Radiance Leatherbound by Brandon Sanderson! backerkit.com/c/projects/dra…

🚀 AWS and Anthropic are pushing the boundaries of generative AI together. Our customers will be able to access the Claude 3 family of vision-enabled models—Claude 3 Opus, Claude 3 Sonnet, and Claude 3 Haiku—on Amazon Bedrock, with Sonnet available today. go.aws/3uTu5db

Model evaluation is critical when selecting FMs for your GenAI use cases. Customers continue to be inspired by Amazon Bedrock Model Evaluation (in preview) for FM comparisons and selection. @awscloud aws.amazon.com/blogs/aws/eva…

Several years ago, when we started pursuing building our own chips, a lot of folks thought this was nuts. We heard a lot of the same refrains you often hear—why make this investment, why invest in a team and all the other fixed costs to develop your own chip when you can buy from other suppliers? And, while we knew we’d partner with those other companies for the foreseeable future, if your customers are telling you they’re thirsty for better price-performance, and you’re driven by what makes customers’ lives better and easier every day, you explore options to make it so. We realized pretty quickly that designing our own chips was going to be the best path to delivering this value for customers. We were lucky to find and join forces with the amazing Annapurna Labs team, who started with a chip (named Nitro) that offloaded security, networking, and some other virtualization functions from our servers so customers could use more of the server than they could before. Then, that team built a generalized CPU chip, Graviton, which has been very popular and impactful for customers, before embarking on building custom AI chips—Trainium (for training) and Inferentia (for inference)—which are also off to a strong start. Am very excited about our most recent chip releases at AWS re: Invent: Graviton4 and Trainium2. Graviton4 marks the fourth generation we’ve delivered in just five years (you can see the evolution from left to right in the image below), and it’s the most powerful and energy efficient multipurpose chip we have built to date. And with the surge of interest in generative AI, Trainium2 will help customers train their ML models faster, at a more advantaged price-performance. I’m really proud of the pace of innovation our teams are delivering on and what it is making possible for customers! aboutamazon.com/news/aws-rei…

It’s Day 1: who’s ready to rock #AWSreInvent? ✋Watch to the end for a glimpse of a future @awscloud builder!

Peter DeSantis kicks off the keynotes, sharing the latest serverless innovations across our database and analytics portfolio. The new capabilities announced today help customers manage data at any scale without spending time and effort provisioning, managing, and scaling their data infrastructure. #AWSreInvent techcrunch.com/2023/11/27/am…