AWS Machine Learning Blog

The following diagram illustrates our solution architecture.

Building a secure search application with access controls using Amazon Kendra

For many enterprises, critical business information is often stored as unstructured data scattered across multiple content repositories. Not only is it challenging for organizations to make this information available to employees when they need it, but it’s also difficult to do so securely so relevant information is available to the right employees or employee groups. […]

Extracting buildings and roads from AWS Open Data using Amazon SageMaker

Sharing data and computing in the cloud allows data users to focus on data analysis rather than data access. Open Data on AWS helps you discover and share public open datasets in the cloud. The Registry of Open Data on AWS hosts a large amount of public open data. The datasets range from genomics to climate to transportation […]

How an important change in web standards impacts your image annotation jobs

Earlier in 2020, widely used browsers like Chrome and Firefox changed their default behavior for rotating images based on image metadata, referred to as EXIF data. Previously, images always displayed in browsers exactly how they’re stored on disk, which is typically unrotated. After the change, images now rotate according to a piece of image metadata […]

How Foxconn built an end-to-end forecasting solution in two months with Amazon Forecast

This is a guest post by Foxconn. The content and opinions in this post are those of the third-party author and AWS is not responsible for the content or accuracy of this post.  In their own words, “Established in Taiwan in 1974, Hon Hai Technology Group (Foxconn) is the world’s largest electronics manufacturer. Foxconn is […]

For an existing data lake registered with Lake Formation, the following diagram illustrates the proposed implementation.

Control and audit data exploration activities with Amazon SageMaker Studio and AWS Lake Formation

May 2024: This post was reviewed and updated to use a new dataset, reflect the updated Studio experience and AWS IAM Identity Center. Certain industries are required to audit all access to their data. This includes auditing exploratory activities performed by data scientists, who usually query data from within machine learning (ML) notebooks. This post […]

Building and deploying an object detection computer vision application at the edge with AWS Panorama

Computer vision (CV) is sought after technology among companies looking to take advantage of machine learning (ML) to improve their business processes. Enterprises have access to large amounts of video assets from their existing cameras, but the data remains largely untapped without the right tools to gain insights from it. CV provides the tools to […]

Population health applications with Amazon HealthLake – Part 1: Analytics and monitoring using Amazon QuickSight

Healthcare has recently been transformed by two remarkable innovations: Medical Interoperability and machine learning (ML). Medical Interoperability refers to the ability to share healthcare information across multiple systems. To take advantage of these transformations, we launched a new HIPAA-eligible healthcare service, Amazon HealthLake, now in preview at re:Invent 2020. In the re:Invent announcement, we talk […]

Focusing on disaster response with Amazon Augmented AI and Mechanical Turk

It’s easy to distinguish a lake from a flood. But when you’re looking at an aerial photograph, factors like angle, altitude, cloud cover, and context can make the task more difficult. And when you need to identify 100,000 aerial images in order to give first responders the information they need to accelerate disaster response efforts? […]

The following diagram illustrates the high-level workflow of Model Monitor.

Monitoring in-production ML models at large scale using Amazon SageMaker Model Monitor

Machine learning (ML) models are impacting business decisions of organizations around the globe, from retail and financial services to autonomous vehicles and space exploration. For these organizations, training and deploying ML models into production is only one step towards achieving business goals. Model performance may degrade over time for several reasons, such as changing consumer […]

Training a reinforcement learning Agent with Unity and Amazon SageMaker RL

Unity is one of the most popular game engines that has been adopted not only for video game development but also by industries such as film and automotive. Unity offers tools to create virtual simulated environments with customizable physics, landscapes, and characters. The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables […]