AWS Machine Learning Blog

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 […]

What’s around the turn in 2021? AWS DeepRacer League announces new divisions, rewards, and community leagues

AWS DeepRacer allows you to get hands on with machine learning (ML) through a fully autonomous 1/18th scale race car driven by reinforcement learning, a 3D racing simulator on the AWS DeepRacer console, a global racing league, and hundreds of customer-initiated community races. The action is already underway at the Championship Cup at AWS re:Invent […]

Private package installation in Amazon SageMaker running in internet-free mode

Amazon SageMaker Studio notebooks and Amazon SageMaker notebook instances are internet-enabled by default. However, many regulated industries, such as financial industries, healthcare, telecommunications, and others, require that network traffic traverses their own Amazon Virtual Private Cloud (Amazon VPC) to restrict and control which traffic can go through public internet. Although you can disable direct internet […]

Securing data analytics with an Amazon SageMaker notebook instance and Kerberized Amazon EMR cluster

Ever since Amazon SageMaker was introduced at AWS re:Invent 2017, customers have used the service to quickly and easily build and train machine learning (ML) models and directly deploy them into a production-ready hosted environment. SageMaker notebook instances provide a powerful, integrated Jupyter notebook interface for easy access to data sources for exploration and analysis. […]