Category: Learning Levels

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.

In this post, we’ll show how anyone in your company can use Amazon Bedrock IDE to quickly create a generative AI chat agent application that analyzes sales performance data. Through simple conversations, business teams can use the chat agent to extract valuable insights from both structured and unstructured data sources without writing code or managing complex data pipelines.

The integration of Amazon SageMaker Studio and Amazon SageMaker HyperPod offers a streamlined solution that provides data scientists and ML engineers with a comprehensive environment that supports the entire ML lifecycle, from development to deployment at scale. In this post, we walk you through the process of scaling your ML workloads using SageMaker Studio and SageMaker HyperPod.

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.

Amazon Finance Automation developed a large language model (LLM)-based question-answer chat assistant on Amazon Bedrock. This solution empowers analysts to rapidly retrieve answers to customer queries, generating prompt responses within the same communication thread. As a result, it drastically reduces the time required to address customer queries. In this post, we share how Amazon Finance Automation built this generative AI Q&A chat assistant using Amazon Bedrock.

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.

In this post, we demonstrate Embodied AI Chess with Amazon Bedrock, bringing a new dimension to traditional chess through generative AI capabilities. Our setup features a smart chess board that can detect moves in real time, paired with two robotic arms executing those moves. Each arm is controlled by different FMs—base or custom. This physical implementation allows you to observe and experiment with how different generative AI models approach complex gaming strategies in real-world chess matches.

In this post, we demonstrate how the Amazon SageMaker model parallel library (SMP) addresses this need through support for new features such as 8-bit floating point (FP8) mixed-precision training for accelerated training performance and context parallelism for processing large input sequence lengths, expanding the list of its existing features.

For enterprises in the realm of cloud computing and software development, providing secure code repositories is essential. As sophisticated cybersecurity threats become more prevalent, organizations must adopt proactive measures to protect their assets. Amazon Bedrock offers a powerful solution by automating the process of scanning repositories for vulnerabilities and remediating them. This post explores how you can use Amazon Bedrock to enhance the security of your repositories and maintain compliance with organizational and regulatory standards.

n this post, we create a virtual analyst that can answer natural language queries of stocks matching certain technical indicator criteria using Amazon Bedrock Agents.