AWS Machine Learning Blog

This post is part of an ongoing series on governing the machine learning (ML) lifecycle at scale. To start from the beginning, refer to Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker. A multi-account strategy is essential not only for improving governance but also for enhancing […]

This post presents an architectural approach to extract data from different cloud environments, such as Google Cloud Platform (GCP) BigQuery, without the need for data movement. This minimizes the complexity and overhead associated with moving data between cloud environments, enabling organizations to access and utilize their disparate data assets for ML projects. We highlight the process of using Amazon Athena Federated Query to extract data from GCP BigQuery, using Amazon SageMaker Data Wrangler to perform data preparation, and then using the prepared data to build ML models within Amazon SageMaker Canvas, a no-code ML interface.

In this post, we present a framework to customize the use of Amazon SageMaker Model Monitor for handling multi-payload inference requests for near real-time inference scenarios. SageMaker Model Monitor monitors the quality of SageMaker ML models in production. Early and proactive detection of deviations in model quality enables you to take corrective actions, such as retraining models, auditing upstream systems, or fixing quality issues without having to monitor models manually or build additional tooling.

In this post, we explore how Planview was able to develop a generative AI assistant to address complex work management process by adopting Amazon Bedrock.

In this post, we will explore building a reusable RAG data pipeline on LangChain—an open source framework for building applications based on LLMs—and integrating it with AWS Glue and Amazon OpenSearch Serverless. The end solution is a reference architecture for scalable RAG indexing and deployment.

In this post, we cover the core concepts behind RAG architectures and discuss strategies for evaluating RAG performance, both quantitatively through metrics and qualitatively by analyzing individual outputs. We outline several practical tips for improving text retrieval, including using hybrid search techniques, enhancing context through data preprocessing, and rewriting queries for better relevance.

In this post, we dive into a business use case for a banking institution. We will show you how a financial or business analyst at a bank can easily predict if a customer’s loan will be fully paid, charged off, or current using a machine learning model that is best for the business problem at hand.

Agentic workflows are a fresh new perspective in building dynamic and complex business use- case based workflows with the help of large language models (LLM) as their reasoning engine or brain. In this post, we set up an agent using Amazon Bedrock Agents to act as a software application builder assistant.

After careful consideration, we made the decision to discontinue Rekognition people pathing on October 31, 2025. New customers will not be able to access the capability effective October 24, 2024, but existing customers will be able to use the capability as normal until October 31, 2025. This post discusses an alternative solution to Rekognition people pathing and how you can implement this solution in your applications.

In this post, we learn how SnapLogic’s Agent Creator leverages Amazon Bedrock to provide a low-code platform that enables enterprises to quickly develop and deploy powerful generative AI applications without deep technical expertise.