AWS Machine Learning Blog

Yes, the AWS re:Invent season is upon us and as always, the place to be is Las Vegas! You marked your calendars, you booked your hotel, and you even purchased the airfare. Now all you need is some guidance on generative AI and machine learning (ML) sessions to attend at this twelfth edition of re:Invent. And although generative AI has appeared in previous events, this year we’re taking it to the next level. In addition to several exciting announcements during keynotes, most of the sessions in our track will feature generative AI in one form or another, so we can truly call our track “Generative AI and ML.” In this post, we give you a sense of how the track is organized and highlight a few sessions we think you’ll like. And although our track focuses on generative AI, many other tracks have related sessions. Use the “Generative AI” tag as you are browsing the session catalog to find them.

The financial service (FinServ) industry has unique generative AI requirements related to domain-specific data, data security, regulatory controls, and industry compliance standards. In addition, customers are looking for choices to select the most performant and cost-effective machine learning (ML) model and the ability to perform necessary customization (fine-tuning) to fit their business use cases. Amazon […]

The IDP Well-Architected Lens is intended for all AWS customers who use AWS to run intelligent document processing (IDP) solutions and are searching for guidance on how to build secure, efficient, and reliable IDP solutions on AWS. Building a production-ready solution in the cloud involves a series of trade-offs between resources, time, customer expectation, and […]

Building a production-ready solution in AWS involves a series of trade-offs between resources, time, customer expectation, and business outcome. The AWS Well-Architected Framework helps you understand the benefits and risks of decisions you make while building workloads on AWS. By using the Framework, you will learn current operational and architectural recommendations for designing and operating […]

The IDP Well-Architected Custom Lens is intended for all AWS customers who use AWS to run intelligent document processing (IDP) solutions and are searching for guidance on how to build a secure, efficient, and reliable IDP solution on AWS. Building a production-ready solution in the cloud involves a series of trade-offs between resources, time, customer […]

When a customer has a production-ready intelligent document processing (IDP) workload, we often receive requests for a Well-Architected review. To build an enterprise solution, developer resources, cost, time and user-experience have to be balanced to achieve the desired business outcome. The AWS Well-Architected Framework provides a systematic way for organizations to learn operational and architectural […]

Building a production-ready solution in the cloud involves a series of trade-off between resources, time, customer expectation, and business outcome. The AWS Well-Architected Framework helps you understand the benefits and risks of decisions you make while building workloads on AWS. An intelligent document processing (IDP) project usually combines optical character recognition (OCR) and natural language […]

An intelligent document processing (IDP) project typically combines optical character recognition (OCR) and natural language processing (NLP) to automatically read and understand documents. Customers across all industries run IDP workloads on AWS to deliver business value by automating use cases such as KYC forms, tax documents, invoices, insurance claims, delivery reports, inventory reports, and more. […]

For decades, Amazon has pioneered and innovated machine learning (ML), bringing delightful experiences to its customers. From the earliest days, Amazon has used ML for various use cases such as book recommendations, search, and fraud detection. Similar to the rest of the industry, the advancements of accelerated hardware have allowed Amazon teams to pursue model […]

Today, geospatial workflows typically consist of loading data, transforming it, and then producing visual insights like maps, text, or charts. Generative AI can automate these tasks through autonomous agents. In this post, we discuss how to use foundation models from Amazon Bedrock to power agents to complete geospatial tasks. These agents can perform various tasks […]