Category: Technical How-to

In Parts 1 and 2 of this series, we explored ways to use the power of multimodal FMs such as Amazon Titan Multimodal Embeddings, Amazon Titan Text Embeddings, and Anthropic’s Claude 3 Sonnet. In this post, we compared the approaches from an accuracy and pricing perspective.

With access to a wide range of generative AI foundation models (FM) and the ability to build and train their own machine learning (ML) models in Amazon SageMaker, users want a seamless and secure way to experiment with and select the models that deliver the most value for their business. In the initial stages of an ML […]

In this post, we show how to build a RAG application on Amazon SageMaker JumpStart using Facebook AI Similarity Search (FAISS).

In this post, we explore how Amazon SageMaker HyperPod training plans accelerate compute resource procurement for machine learning workloads. We guide you through a step-by-step implementation on how you can use the AWS CLI or the AWS Management Console to find, review, and create optimal training plans for your specific compute and timeline needs. We further guide you through using the training plan to submit SageMaker training jobs or create SageMaker HyperPod clusters.

In this post, we discuss how Amazon SageMaker and Tecton work together to simplify the development and deployment of production-ready AI applications, particularly for real-time use cases like fraud detection. The integration enables faster time to value by abstracting away complex engineering tasks, allowing teams to focus on building features and use cases while providing a streamlined framework for both offline training and online serving of ML models.

Chronos models are available for Amazon SageMaker customers through AutoGluon-TimeSeries and Amazon SageMaker JumpStart. In this post, we introduce Chronos-Bolt, our latest FM for forecasting that has been integrated into AutoGluon-TimeSeries.

Today, we are excited to announce the availability of Prompt Optimization on Amazon Bedrock. With this capability, you can now optimize your prompts for several use cases with a single API call or a click of a button on the Amazon Bedrock console. In this post, we discuss how you can get started with this new feature using an example use case in addition to discussing some performance benchmarks.

In this post, we present a generative AI-powered semantic search solution that empowers business users to quickly and accurately find relevant data assets across various enterprise data sources. In this solution, we integrate large language models (LLMs) hosted on Amazon Bedrock backed by a knowledge base that is derived from a knowledge graph built on Amazon Neptune to create a powerful search paradigm that enables natural language-based questions to integrate search across documents stored in Amazon Simple Storage Service (Amazon S3), data lake tables hosted on the AWS Glue Data Catalog, and enterprise assets in Amazon DataZone.

Seamless integration of customer experience, collaboration tools, and relevant data is the foundation for delivering knowledge-based productivity gains. In this post, we show you how to integrate the popular Slack messaging service with AWS generative AI services to build a natural language assistant where business users can ask questions of an unstructured dataset.

In this post, we walk through the steps to deploy the Meta Llama 3.1-8B model on Inferentia 2 instances using Amazon EKS. This solution combines the exceptional performance and cost-effectiveness of Inferentia 2 chips with the robust and flexible landscape of Amazon EKS. Inferentia 2 chips deliver high throughput and low latency inference, ideal for LLMs.