AWS Machine Learning Blog
Category: Amazon SageMaker
ML Explainability with Amazon SageMaker Debugger
Machine Learning (ML) impacts industries around the globe, from financial services industry (FSI) and manufacturing to autonomous vehicles and space exploration. ML is no longer just an aspirational technology exclusive to academic and research institutions; it has evolved into a mainstream technology that has the potential to benefit organizations of all sizes. However, a lack […]
Scaling your AI-powered Battlesnake with distributed reinforcement learning in Amazon SageMaker
Battlesnake is an AI competition in which you build AI-powered snakes. Battlesnake’s rules are similar to the traditional snakes game. Your goal is to be the last surviving snake when competing against other snakes. Developers of all levels build snakes using techniques ranging from unique heuristic-based strategies to state-of-the-art deep reinforcement learning (RL) algorithms. You […]
Announcing availability of Inf1 instances in Amazon SageMaker for high performance and cost-effective machine learning inference
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Tens of thousands of customers, including Intuit, Voodoo, ADP, Cerner, Dow Jones, and Thompson Reuters, use Amazon SageMaker to remove the heavy lifting from each step of the […]
Bring your own model for Amazon SageMaker labeling workflows with active learning
With Amazon SageMaker Ground Truth, you can easily and inexpensively build accurately labeled machine learning (ML) datasets. To decrease labeling costs, SageMaker Ground Truth uses active learning to differentiate between data objects (like images or documents) that are difficult and easy to label. Difficult data objects are sent to human workers to be annotated and […]
Reducing player wait time and right sizing compute allocation using Amazon SageMaker RL and Amazon EKS
As a multiplayer game publisher, you may often need to either over-provision resources or manually manage compute allocation when launching or maintaining an online game to avoid long player wait times. You need to develop, configure, and deploy tools that help you monitor and control the compute allocation. This post demonstrates GameServer Autopilot, a new […]
Autodesk optimizes visual similarity search model in Fusion 360 with Amazon SageMaker Debugger
This post is co-written by Alexander Carlson, a machine learning engineer at Autodesk. Autodesk started its digital transformation journey years ago by moving workloads from private data centers to AWS services. The benefits of digital transformation are clear with generative design, which is a new technology that uses cloud computing to accelerate design exploration beyond […]
Pruning machine learning models with Amazon SageMaker Debugger and Amazon SageMaker Experiments
In the past decade, deep learning has advanced many different areas, such as computer vision and natural language processing. State-of-the-art models now achieve near-human performance in tasks such as image classification. Deep neural networks can achieve this because they consist of millions of parameters that you train on large training datasets. For instance, the BERT […]
Increasing performance and reducing the cost of MXNet inference using Amazon SageMaker Neo and Amazon Elastic Inference
Note: Amazon Elastic Inference is no longer available. Please see Amazon SageMaker for similar capabilities. When running deep learning models in production, balancing infrastructure cost versus model latency is always an important consideration. At re:Invent 2018, AWS introduced Amazon SageMaker Neo and Amazon Elastic Inference, two services that can make models more efficient for deep […]
Building a trash sorter with AWS DeepLens
April 2023 Update: Starting January 31, 2024, you will no longer be able to access AWS DeepLens through the AWS management console, manage DeepLens devices, or access any projects you have created. To learn more, refer to these frequently asked questions about AWS DeepLens end of life. In this blog post, we show you how to […]
Building an AI-powered Battlesnake with reinforcement learning on Amazon SageMaker
Battlesnake is an AI competition based on the traditional snake game in which multiple AI-powered snakes compete to be the last snake surviving. Battlesnake attracts a community of developers at all levels. Hundreds of snakes compete and rise up in the ranks in the online Battlesnake global arena. Battlesnake also hosts several offline events that […]