AWS Machine Learning Blog

Amazon Kendra customers can now enrich document metadata and content during the document ingestion process using custom document enrichment (CDE). Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra reimagines search for your websites and applications so your employees and customers can easily find the content they’re looking for, even […]

Unstructured data belonging to enterprises continues to grow, making it a challenge for customers and employees to get the information they need. Amazon Kendra is a highly accurate intelligent search service powered by machine learning (ML). It helps you easily find the content you’re looking for, even when it’s scattered across multiple locations and content […]

Update June 20th 2022: The Automated Chatbot Designer is now generally available. Since the preview launch, we have improved quality of intent recommendations and diversity of utterances, introduced a click-through experience, and added usability enhancements. These updates, further reduces time and effort it takes to design a chatbot. Today, we’re launching the Amazon Lex automated […]

Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra reimagines search for your websites and applications so your employees and customers can easily find the content they’re looking for, even when it’s scattered across multiple locations and content repositories within your organization. With Amazon Kendra, you don’t need to click […]

In Part 1 of this series, we offered step-by-step guidance for creating, connecting, stopping, and debugging Amazon EMR clusters from Amazon SageMaker Studio in a single-account setup. In this post, we dive deep into how you can use the same functionality in certain enterprise-ready, multi-account setups. As described in the AWS Well-Architected Framework, separating workloads […]

February 2024: This blog post was reviewed and updated to include an updated AWS CloudFormation stack to comply with a recent Python3.7 lambda deprecation policy. Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). It provides a single, web-based visual interface where you can perform all ML development steps, […]

Today, we’re excited to announce intelligent user segmentation powered by machine learning (ML) in Amazon Personalize, a new way to deliver personalized experiences to your users and run more effective campaigns through your marketing channels. Traditionally, user segmentation depends on demographic or psychographic information to sort users into predefined audiences. More advanced techniques look to […]

Today, we’re excited to announce the launch of personalized recommenders in Amazon Personalize that are optimized for retail and media and entertainment, making it even easier to personalize your websites, apps, and marketing campaigns. With this launch, we have drawn on Amazon’s rich experience creating unique personalized user experiences using machine learning (ML) to build […]

Machine learning operations (MLOps) are key to effectively transition from an experimentation phase to production. The practice provides you the ability to create a repeatable mechanism to build, train, deploy, and manage machine learning models. To quickly adopt MLOps, you often require capabilities that use your existing toolsets and expertise. Projects in Amazon SageMaker give […]

This is a guest post by Lucas Baker, Andrea Duque, and Viet Yen Nguyen of Hypefactors.   At Hypefactors, we build tech for media intelligence and reputation management. The solution is a software as a service (SaaS) product that does large-scale media monitoring of social media, news sites, TV, radio, and reviews across the world. The […]