Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML.
The most comprehensive ML service
Accelerate innovation with purpose-built tools for every step of ML development, including labeling, data preparation, feature engineering, statistical bias detection, auto-ML, training, tuning, hosting, explainability, monitoring, and workflows.
How it works
Amazon SageMaker supports the leading machine learning frameworks, toolkits and programming languages
Build, train, and tune models automatically
Amazon SageMaker Autopilot selects the best algorithm for the prediction, and automatically builds, trains, and tunes machine learning models without any loss of visibility or control.
Reduce data labeling costs by up to 70%
Amazon SageMaker Ground Truth makes it easy to more accurately label training datasets for a variety of use cases including 3D point clouds, video, images, and text.
The fastest and easiest way to prepare data for ML
Amazon SageMaker Data Wrangler reduces the time it takes to prepare data for ML from weeks to minutes. With a few clicks, you can complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization.
Purpose-built feature store for ML
Amazon SageMaker Feature Store provides a repository to store, update, retrieve, and share ML features. SageMaker Feature Store offers one consistent view of features for ML models to use so it becomes significantly easier to generate models that produce highly accurate predictions.
Train high-quality models faster
Amazon SageMaker provides a built-in debugger and a profiler so you can identify and reduce training errors and performance bottlenecks from your models before pushing them to production.
One-click deployment to the cloud
Amazon SageMaker makes it easy to deploy your trained model to production with a single click, so you can start generating predictions for real-time or batch data.
Improve quality of models on edge devices
Amazon SageMaker Edge Manager helps you optimize, secure, monitor, and maintain machine learning models on fleets of edge devices to ensure that models deployed on edge devices are operating correctly.
Get started with Amazon SageMaker JumpStart
Amazon SageMaker is a machine learning service that you can use to build, train, and deploy ML models for virtually any use case. For a quick technical introduction, see the SageMaker step-by-step guide. To help you get started with your ML project, Amazon SageMaker JumpStart offers a set of pre-built solutions for the most common use cases that you can deploy with just a few clicks. These solutions are fully customizable so you can modify them to suit the needs of your specific use case and datasets.
Georgia Pacific uses SageMaker to develop ML models that detect machine issues early.
3M is using defect detection models built on SageMaker to improve the effectiveness of its quality control processes.
Lyft Level 5 standardized on SageMaker for training and reduced model training times from days to under a couple of hours.