Category: Amazon Machine Learning

This post outlines the best practices for provisioning Amazon SageMaker Studio for data science teams and provides reference architectures and AWS CloudFormation templates to help you get started. We use AWS Service Catalog to provision a Studio domain and users. The AWS Service Catalog allows you to provision these centrally without requiring each user to […]

In this post, you learn how to create a MLOps project to automate the deployment of an Amazon SageMaker endpoint with multiple production variants for A/B testing. You also deploy a general purpose API and testing infrastructure that includes a multi-armed bandit experiment framework. This testing infrastructure will automatically optimize traffic to the best-performing model […]

Amazon SageMaker helps data scientists and developers prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. SageMaker accelerates innovation within your organization by providing purpose-built tools for every step of ML development, including labeling, data preparation, feature engineering, statistical bias detection, AutoML, […]

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 post, we show you how to use […]

Support for AWS DeepComposer will be ending soon. Please see Support for AWS DeepComposer ending soon for more details. At AWS, we love sharing our passion for technology and innovation, and AWS DeepComposer is no exception. This service is designed to help everyone learn about generative artificial intelligence (AI) through the language of music. You […]

Hugging Face is the technology startup, with an active open-source community, that drove the worldwide adoption of transformer-based models thanks to its eponymous Transformers library. Earlier this year, Hugging Face and AWS collaborated to enable you to train and deploy over 10,000 pre-trained models on Amazon SageMaker. For more information on training Hugging Face models […]

As the prevalence of machine learning (ML) and artificial intelligence (AI) grows, you need the best mechanisms to aid in the experimentation and development of your algorithms. You might begin with the several built-in algorithms in Amazon SageMaker that simply require you to point the algorithm at your data and start a SageMaker training job. […]

In this post, we look at how we can automate the detection of anomalies in a manufactured product using Amazon Lookout for Vision. Using Amazon Lookout for Vision, you can notify operators in real time when defects are detected, provide dashboards for monitoring the workload, and get visual insights from the process for business users. […]

There is nothing more exhilarating than getting the keys to your first car or driving off the lot with the car of your dreams. Sadly, that exhilaration can quickly fade to frustration when your car is damaged. Working through the phone calls, emails, and damage reports with your insurance provider can be a painstaking process. […]

AWS customers are increasingly using computer vision (CV) models for improved efficiency and an enhanced user experience. For example, a live broadcast of sports can be processed in real time to detect specific events automatically and provide additional insights to viewers at low latency. Inventory inspection at large warehouses capture and process millions of images […]