Amazon SageMaker

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

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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

Choice of ML tools

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

Fully managed, scalable infrastructure

Build your own ML models, including FMs to power generative AI applications, with integrated purpose-built tools and high performance, cost effective infrastructure.

Repeatable and responsible ML workflows

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

Human-in-the-loop

Harness the power of human feedback across the ML lifecycle to improve the accuracy and relevancy of FMs with human-in-the-loop capabilities.

Generative AI assistance

Get assistance throughout the entire ML development journey from data preparation and model training, to model deployment. Amazon Q Developer can generate code suggestions, answer your questions, and offer troubleshooting assistance when you encounter errors.

SageMaker pricing and AWS Free Tier

The SageMaker AWS Free Tier offers a 2-month free trial that provides 250 hours per month of t2.medium or t3.medium notebook usage, 50 hours per month of m4.xlarge or m5.xlarge for training, and 125 hours per month of m4.xlarge or m5.xlarge for hosting. To learn more about the SageMaker AWS Free Tier offering and cost-effective pricing options, visit the SageMaker pricing page.

Using Amazon SageMaker for generative AI

Amazon SageMaker helps data scientists and ML engineers build FMs from scratch, evaluate and customize FMs with advanced techniques, and deploy FMs with fine-grain controls for generative AI use cases that have stringent requirements on accuracy, latency, and cost.

Build foundation models from scratch

Amazon SageMaker offers tools to pre-train FMs from scratch so they can be used internally or offered to other businesses for generative AI applications.

Customize foundation models with advanced techniques

Amazon SageMaker provides access to hundreds of publicly available FMs, and tools to evaluate and fully customize models for your specific use case and data.