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
Amazon SageMaker Pricing and Free Tier
SageMaker's 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, 125 hours per month of m4.xlarge or m5.xlarge for hosting. To learn more about SageMaker's 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.
Deploy foundation models for inference
Amazon SageMaker makes it easy to deploy FMs to make inference requests at the best price-performance for any use case.
Support for the leading ML frameworks, toolkits, and programming languages
![Jupyter Jupyter logo](https://d1.awsstatic.com/sagemaker/2022whiteupdate/overview-page/6-28-resizing-logos-and-frameworks/Jupyter_logo_103x130.78ab3fabbd3c2776c84fae16b49fc25ccf1a3476.png)
![TensorFlow TensorFlow logo](https://d1.awsstatic.com/sagemaker/2022whiteupdate/overview-page/6-28-resizing-logos-and-frameworks/TensorFlow_logo_103x130.b6277ea4450f113a2bb480421be5196a6280e81a.png)
![PyTorch PyTorch logo](https://d1.awsstatic.com/sagemaker/2022whiteupdate/overview-page/6-28-resizing-logos-and-frameworks/Pytorch_logo_103x130.0f840268c001a59d09bb5bee56624b3e356c2334.png)
![MXNet MXNet logo](https://d1.awsstatic.com/sagemaker/2022whiteupdate/overview-page/6-28-resizing-logos-and-frameworks/mxnet_logo_103x130.c8e4a357320756803c8af55516f3f50e19120979.png)
![Hugging Face Hugging Face logo](https://d1.awsstatic.com/sagemaker/2022whiteupdate/overview-page/6-28-resizing-logos-and-frameworks/Huggingface_103x130.739723088a97e4178c741fb9932f4be75121df8b.png)
![Scikit-learn Scikit-learn logo](https://d1.awsstatic.com/sagemaker/2022whiteupdate/overview-page/6-28-resizing-logos-and-frameworks/Scikit_learn_logo_103x130.2cab88340ff0b9324daf08ab5de7940d5f239974.png)
![Python Python logo](https://d1.awsstatic.com/sagemaker/2022whiteupdate/overview-page/6-28-resizing-logos-and-frameworks/python-logo-103x130.e9f7f0a0bbf07487b518d5e73fc4287401ce4699.png)
![R R logo](https://d1.awsstatic.com/sagemaker/2022whiteupdate/overview-page/6-28-resizing-logos-and-frameworks/R_logo_103x130.abc18c475ca936e84e728d3cc6da1d6a47be95ed.png)
High-performance, low-cost ML at scale
1.5 trillion+
inference requests per month40%
reduction in data labeling costs<10ms
inference overhead latencyDid you find what you were looking for today?
Let us know so we can improve the quality of the content on our pages.