AWS Machine Learning Blog
Category: Technical How-to
Achieve multi-Region resiliency for your conversational AI chatbots with Amazon Lex
Global Resiliency is a new Amazon Lex capability that enables near real-time replication of your Amazon Lex V2 bots in a second AWS Region. When you activate this feature, all resources, versions, and aliases associated after activation will be synchronized across the chosen Regions. With Global Resiliency, the replicated bot resources and aliases in the […]
Automate document processing with Amazon Bedrock Prompt Flows (preview)
This post demonstrates how to build an IDP pipeline for automatically extracting and processing data from documents using Amazon Bedrock Prompt Flows, a fully managed service that enables you to build generative AI workflow using Amazon Bedrock and other services in an intuitive visual builder. Amazon Bedrock Prompt Flows allows you to quickly update your pipelines as your business changes, scaling your document processing workflows to help meet evolving demands.
Import data from Google Cloud Platform BigQuery for no-code machine learning with Amazon SageMaker Canvas
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.
Customized model monitoring for near real-time batch inference with Amazon SageMaker
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.
Super charge your LLMs with RAG at scale using AWS Glue for Apache Spark
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.
From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 1
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.
Enhance your Amazon Redshift cloud data warehouse with easier, simpler, and faster machine learning using Amazon SageMaker Canvas
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.
Boost post-call analytics with Amazon Q in QuickSight
In this post, we show you how to unlock powerful post-call analytics and visualizations, empowering your organization to make data-driven decisions and drive continuous improvement.
Automate fine-tuning of Llama 3.x models with the new visual designer for Amazon SageMaker Pipelines
In this post, we will show you how to set up an automated LLM customization (fine-tuning) workflow so that the Llama 3.x models from Meta can provide a high-quality summary of SEC filings for financial applications. Fine-tuning allows you to configure LLMs to achieve improved performance on your domain-specific tasks.
Implement Amazon SageMaker domain cross-Region disaster recovery using custom Amazon EFS instances
In this post, we guide you through a step-by-step process to seamlessly migrate and safeguard your SageMaker domain from one active Region to another passive or active Region, including all associated user profiles and files.