Amazon SageMaker

Machine learning for every data scientist and developer

Amazon SageMaker Studio

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, auto-ML, training, tuning, hosting, monitoring, and workflows.

Machine Learning Workflow
IDE for ML

The first integrated development environment (IDE) for ML

Boost your productivity using Amazon SageMaker Studio, the first fully integrated development environment designed specifically for ML that brings everything you need for ML under one unified, visual user interface.

Integrated functionality

Functionality designed from the ground up to work together

Use Amazon SageMaker’s integrated capabilities for ML development, so you can eliminate months of writing custom integration code, and ultimately reduce cost.

How it works

  • Overview
  • Details

One of the fastest growing services in AWS history

Amazon SageMaker is built on Amazon’s two decades of experience developing real-world machine learning applications, including product recommendations, personalization, intelligent shopping, robotics, and voice-assisted devices.


increase in team productivity


cost reduction with managed spot training


lower inference costs


lower TCO


reduction in data labeling costs


new capabilities added since launch


compliance programs (PCI, HIPAA, SOC 1/2/3, FedRAMP, ISO, and more)

Amazon SageMaker supports the leading machine learning frameworks


Key features to prepare data, and build, train, and deploy ML models

Improve productivity using the first fully integrated development environment (IDE) for ML

Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps required to prepare data, and build, train, and deploy models.

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SageMaker Studio

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.

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SageMaker Autopilot

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.

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SageMaker Ground Truth

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.

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SageMaker Data Wrangler

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.

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SageMaker Feature Store

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.

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One-click deploy

Essential features for ML in production

SageMaker Pipelines

Automate machine learning workflows

Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning. Workflows can be shared and re-used between teams.

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SageMaker Security

Secure your data and code throughout the ML lifecycle

Amazon SageMaker offers a comprehensive set of security features, including encryption, private network connectivity, authorization, authentication, monitoring, and auditability to help your organization with security requirements that may apply to machine learning workloads.

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Amazon SageMaker customers

Amazon SageMaker is used by tens of thousands of customers across a wide range of industries.

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Capital One
Conde Nast
Thomson Reuters

Get started with Amazon SageMaker

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, AWS 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.

Pre-built solutions » SageMaker step-by-step guide »
Predictive maintenance

Predictive maintenance

Georgia Pacific uses SageMaker to develop ML models that detect machine issues early.

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Computer vision

Computer vision

3M is using defect detection models built on SageMaker to improve the effectiveness of its quality control processes.

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Autonomous driving

Autonomous driving

Lyft Level 5 standardized on SageMaker for training and reduced model training times from days to under a couple of hours.

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