AWS Machine Learning Blog

Category: SageMaker

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 […]

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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 […]

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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 […]

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Building a trash sorter with AWS DeepLens

In this blog post, we show you how to build a prototype trash sorter using AWS DeepLens, the AWS deep learning-enabled video camera designed for developers to learn machine learning in a fun, hands-on way. This prototype trash sorter project teaches you how to train image classification models with custom data. Image classification is a […]

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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 […]

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Creating a machine learning-powered REST API with Amazon API Gateway mapping templates and Amazon SageMaker

Amazon SageMaker enables organizations to build, train, and deploy machine learning models. Consumer-facing organizations can use it to enrich their customers’ experiences, for example, by making personalized product recommendations, or by automatically tailoring application behavior based on customers’ observed preferences. When building such applications, one key architectural consideration is how to make the runtime inference […]

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Training batch reinforcement learning policies with Amazon SageMaker RL

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. In addition to building ML models using more commonly used supervised and unsupervised learning techniques, you can also build reinforcement learning (RL) models using Amazon SageMaker RL. […]

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Using DeepChem with Amazon SageMaker for virtual screening

Virtual screening is a computational methodology used in drug or materials discovery by searching a vast amount of molecules libraries to identify the structures that are most likely to show the target characteristics. It is becoming a ground-breaking tool for molecular discovery due to the exponential growth of available computer time and constant improvement of […]

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Simplify Machine Learning Inference on Kubernetes with Amazon SageMaker Operators

Amazon SageMaker Operators for Kubernetes allows you to augment your existing Kubernetes cluster with SageMaker hosted endpoints. Machine learning inferencing requires investment to create a reliable and efficient service. For an XGBoost model, developers have to create an application, such as through Flask that will load the model and then run the endpoint, which requires […]

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Automating model retraining and deployment using the AWS Step Functions Data Science SDK for Amazon SageMaker

As machine learning (ML) becomes a larger part of companies’ core business, there is a greater emphasis on reducing the time from model creation to deployment. In November of 2019, AWS released the AWS Step Functions Data Science SDK for Amazon SageMaker, an open-source SDK that allows developers to create Step Functions-based machine learning workflows […]

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