AWS Machine Learning Blog

In this post, we demonstrate how to use the Open AI Whisper foundation model (FM) Whisper Large V3 Turbo, available in Amazon Bedrock Marketplace, which offers access to over 140 models through a dedicated offering, to produce near real-time transcription. These transcriptions are then processed by Amazon Bedrock for summarization and redaction of sensitive information.

In this post, we demonstrate how to use large vision models (LVMs) for semantic video search using natural language and image queries. We introduce some use case-specific methods, such as temporal frame smoothing and clustering, to enhance the video search performance. Furthermore, we demonstrate the end-to-end functionality of this approach by using both asynchronous and real-time hosting options on Amazon SageMaker AI to perform video, image, and text processing using publicly available LVMs on the Hugging Face Model Hub. Finally, we use Amazon OpenSearch Serverless with its vector engine for low-latency semantic video search.

In this post, we discuss how an enterprise with multiple accounts can access a shared Amazon SageMaker HyperPod cluster for running their heterogenous workloads. We use SageMaker HyperPod task governance to enable this feature.

This post evaluates the key options for querying data using generative AI, discusses their strengths and limitations, and demonstrates why Text-to-SQL is the best choice for deterministic, schema-specific tasks. We show how to effectively use Text-to-SQL using Amazon Nova, a foundation model (FM) available in Amazon Bedrock, to derive precise and reliable answers from your data.

Radial is the largest 3PL fulfillment provider, also offering integrated payment, fraud detection, and omnichannel solutions to mid-market and enterprise brands. In this post, we share how Radial optimized the cost and performance of their fraud detection machine learning (ML) applications by modernizing their ML workflow using Amazon SageMaker.

Contextual retrieval enhances traditional RAG by adding chunk-specific explanatory context to each chunk before generating embeddings. This approach enriches the vector representation with relevant contextual information, enabling more accurate retrieval of semantically related content when responding to user queries. In this post, we demonstrate how to use contextual retrieval with Anthropic and Amazon Bedrock Knowledge Bases.

In this post, we demonstrate how to deploy a small language model on SageMaker AI by extending our pre-built containers to be compatible with AWS Graviton instances. We first provide an overview of the solution, and then provide detailed implementation steps to help you get started. You can find the example notebook in the GitHub repo.

In this post, we share how Impel enhances the automotive dealership customer experience with fine-tuned LLMs on SageMaker.

In this post, we show how climate tech startups are developing foundation models (FMs) that use extensive environmental datasets to tackle issues such as carbon capture, carbon-negative fuels, new materials design for microplastics destruction, and ecosystem preservation. These specialized models require advanced computational capabilities to process and analyze vast amounts of data effectively.

In this post, we’ll explore how to combine Amazon Bedrock prompt caching with Claude Code—a coding agent released by Anthropic that is now generally available. This powerful combination transforms your development workflow by delivering lightning-fast responses from reducing inference response latency, as well as lowering input token costs.