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We are excited to announce a new version of the Amazon SageMaker Operators for Kubernetes using the AWS Controllers for Kubernetes (ACK). ACK is a framework for building Kubernetes custom controllers, where each controller communicates with an AWS service API. These controllers allow Kubernetes users to provision AWS resources like buckets, databases, or message queues …

Use Kubernetes Operators for new inference capabilities in Amazon SageMaker that reduce LLM deployment costs by 50% on average Read More »

In Part 1 of this series, we presented a solution that used the Amazon Titan Multimodal Embeddings model to convert individual slides from a slide deck into embeddings. We stored the embeddings in a vector database and then used the Large Language-and-Vision Assistant (LLaVA 1.5-7b) model to generate text responses to user questions based on …

Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 2 Read More »

This is a guest post co-written with the leadership team of Iambic Therapeutics. Iambic Therapeutics is a drug discovery startup with a mission to create innovative AI-driven technologies to bring better medicines to cancer patients, faster. Our advanced generative and predictive artificial intelligence (AI) tools enable us to search the vast space of possible drug …

Scale AI training and inference for drug discovery through Amazon EKS and Karpenter Read More »

Migrating to the cloud is an essential step for modern organizations aiming to capitalize on the flexibility and scale of cloud resources. Tools like Terraform and AWS CloudFormation are pivotal for such transitions, offering infrastructure as code (IaC) capabilities that define and manage complex cloud environments with precision. However, despite its benefits, IaC’s learning curve, …

Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock Read More »

See CHANGELOG for latest features and fixes. You’ve likely experienced the challenge of taking notes during a meeting while trying to pay attention to the conversation. You’ve probably also experienced the need to quickly fact-check something that’s been said, or look up information to answer a question that’s just been asked in the call. Or …

Live Meeting Assistant with Amazon Transcribe, Amazon Bedrock, and Knowledge Bases for Amazon Bedrock Read More »

Today, we are excited to announce that Meta Llama 3 foundation models are available through Amazon SageMaker JumpStart to deploy and run inference. The Llama 3 models are a collection of pre-trained and fine-tuned generative text models. In this post, we walk through how to discover and deploy Llama 3 models via SageMaker JumpStart. What is …

Meta Llama 3 models are now available in Amazon SageMaker JumpStart Read More »

This post is co-authored by Jackie Rocca, VP of Product, AI at Slack Slack is where work happens. It’s the AI-powered platform for work that connects people, conversations, apps, and systems together in one place. With the newly launched Slack AI—a trusted, native, generative artificial intelligence (AI) experience available directly in Slack—users can surface and …

Slack delivers native and secure generative AI powered by Amazon SageMaker JumpStart Read More »

In asset management, portfolio managers need to closely monitor companies in their investment universe to identify risks and opportunities, and guide investment decisions. Tracking direct events like earnings reports or credit downgrades is straightforward—you can set up alerts to notify managers of news containing company names. However, detecting second and third-order impacts arising from events …

Uncover hidden connections in unstructured financial data with Amazon Bedrock and Amazon Neptune Read More »

Recent developments in machine learning (ML) have led to increasingly large models, some of which require hundreds of billions of parameters. Although they are more powerful, training and inference on those models require significant computational resources. Despite the availability of advanced distributed training libraries, it’s common for training and inference jobs to need hundreds of …

Open source observability for AWS Inferentia nodes within Amazon EKS clusters Read More »

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them. They then use SQL to explore, analyze, visualize, and integrate …

Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks Read More »