AWS Machine Learning Blog

Incremental learning: Optimizing search relevance at scale using machine learning

Amazon Kendra is releasing incremental learning to automatically improve search relevance and make sure you can continuously find the information you’re looking for, particularly when search patterns and document trends change over time. Data proliferation is real, and it’s growing. In fact, International Data Corporation (IDC) predicts that 80% of all data will be unstructured […]

A diagram showing how to Choose create a data source

Getting started with the Amazon Kendra Google Drive connector

Amazon Kendra is a highly accurate and easy-to-use intelligent search service powered by machine learning (ML). To simplify the process of connecting data sources to your index, Amazon Kendra offers several native data source connectors to help get your documents easily ingested. For many organizations, Google Drive is a core part of their productivity suite, […]

How Thomson Reuters accelerated research and development of natural language processing solutions with Amazon SageMaker

This post is co-written by John Duprey and Filippo Pompili from Thomson Reuters. Thomson Reuters (TR) is one of the world’s most trusted providers of answers, helping professionals make confident decisions and run better businesses. Teams of experts from TR bring together information, innovation, and confident insights to unravel complex situations, and their worldwide network […]

Using a test framework to design better experiences with Amazon Lex

November 2022: This post was updated to work for Amazon Lex V2. Chatbots have become an increasingly important channel for businesses to service their customers. Chatbots provide 24/7 availability and can help customers interact with brands anywhere, anytime and on any device. To effectively utilize chatbots, they must be built with good design, development, test, […]

Automated model refresh with streaming data

In today’s world, being able to quickly bring on-premises machine learning (ML) models to the cloud is an integral part of any cloud migration journey. This post provides a step-by-step guide for launching a solution that facilitates the migration journey for large-scale ML workflows. This solution was developed by the Amazon ML Solutions Lab for […]

Performing simulations at scale with Amazon SageMaker Processing and R on RStudio

Statistical analysis and simulation are prevalent techniques employed in various fields, such as healthcare, life science, and financial services. The open-source statistical language R and its rich ecosystem with more than 16,000 packages has been a top choice for statisticians, quant analysts, data scientists, and machine learning (ML) engineers. RStudio is an integrated development environment […]

Delivering operational insights directly to your on-call team by integrating Amazon DevOps Guru with Atlassian Opsgenie

As organizations continue to adopt microservices, the number of disparate services that contribute to delivering applications increases, driving the scope of signals that on-call teams monitor to grow exponentially. It’s becoming more important than ever for these teams to have tools that can quickly and autonomously detect anomalous behaviors across the services they support. Amazon […]

Introducing AWS Panorama – Improve your operations with computer vision at the edge

Yesterday at AWS re:Invent 2020, we announced AWS Panorama, a new machine learning (ML) Appliance and SDK, which allows organizations to bring computer vision (CV) to their on-premises cameras to make automated predictions with high accuracy and low latency. In this post, you learn how customers across a range of industries are using AWS Panorama […]

Introducing the AWS Panorama Device SDK: Scaling computer vision at the edge with AWS Panorama-enabled devices

Yesterday, at AWS re:Invent, we announced AWS Panorama, a new Appliance and Device SDK that allows organizations to bring computer vision to their on-premises cameras to make automated predictions with high accuracy and low latency. With AWS Panorama, companies can use compute power at the edge (without requiring video streamed to the cloud) to improve […]

Configuring autoscaling inference endpoints in Amazon SageMaker

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to quickly build, train, and deploy machine learning (ML) models at scale. Amazon SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality models. You can one-click deploy your […]