Amazon SageMaker

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

Why SageMaker?

Amazon SageMaker is a fully managed service that brings together a broad set of tools to enable high-performance, low-cost machine learning (ML) for any use case. With SageMaker, you can build, train and deploy ML models at scale using tools like notebooks, debuggers, profilers, pipelines, MLOps, and more – all in one integrated development environment (IDE). SageMaker supports governance requirements with simplified access control and transparency over your ML projects. In addition, you can build your own FMs, large models that were trained on massive datasets, with purpose-built tools to fine-tune, experiment, retrain, and deploy FMs. SageMaker offers access to hundreds of pretrained models, including publicly available FMs, that you can deploy with just a few clicks.

Benefits of SageMaker

Enable more people to innovate with ML through a choice of tools—IDEs for data scientists and no-code interface for business analysts.
Build your own ML models, including FMs to power generative AI applications, with integrated purpose-built tools and high performance, cost effective infrastructure.
Automate and standardize MLOps practices and governance across your organization to support transparency and auditability.
Harness the power of human feedback across the ML lifecycle to improve the accuracy and relevancy of FMs with human-in-the-loop capabilities.

Enable more people to innovate with ML

Business analysts

Make ML predictions using a visual interface with SageMaker Canvas.

Image depicts the creation of a new model in Amazon SageMaker Canvas

Data scientists

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

Image displaying a screen on Amazon SageMaker Studio

ML engineers

Deploy and manage models at scale with SageMaker MLOps.

Image displaying a screen on Amazon SageMaker Studio

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

Jupyter logo
TensorFlow logo
PyTorch logo
MXNet logo
Hugging Face logo
Scikit-learn logo
Python logo
R logo

High-performance, low-cost ML at scale

1.5 trillion+

inference requests per month


reduction in data labeling costs


inference overhead latency