Amazon SageMaker

Build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows

Enable more people to innovate with ML through a choice of tools—IDEs for data scientists and no-code interface for business analysts.

Access, label, and process large amounts of structured data (tabular data) and unstructured data (photo, video, geospatial, and audio) for ML.

Reduce training time from hours to minutes with optimized infrastructure. Boost team productivity up to 10 times with purpose-built tools.

Automate and standardize MLOps practices and governance across your organization to support transparency and auditability.

Enable more people to innovate with ML

Business analysts

Make ML predictions using a visual interface with SageMaker Canvas.

SageMaker for business analysts »

Data scientists

Prepare data and build, train, and deploy models with SageMaker Studio.

SageMaker for data scientists »

SageMaker for MLOps Engineers illustration

ML engineers

Deploy and manage models at scale with SageMaker MLOps.

SageMaker for ML engineers »

Support for the leading ML frameworks, toolkits, and programming languages

Build with foundation models

Customize foundation models in Amazon Bedrock using a complete set of ML tools including Amazon SageMaker Experiments to test different models and Amazon SageMaker Pipelines to create, automate, and manage end-to-end ML workflows at scale.

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High-performance, low-cost ML at scale

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

10x

increase in team productivity

1 trillion+

predictions per month

54%

lower TCO

40%

reduction in data labeling costs

Up to 50%

faster training through more efficient use of GPUs

<10ms

inference overhead latency