What is Amazon SageMaker? - Amazon SageMaker
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What is Amazon SageMaker?

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and confidently build, train, and deploy ML models into a production-ready hosted environment. It provides a UI experience for running ML workflows that makes SageMaker ML tools available across multiple integrated development environments (IDEs).

With SageMaker, you can store and share your data without having to build and manage your own servers. This gives you or your organizations more time to collaboratively build and develop your ML workflow, and do it sooner. SageMaker provides managed ML algorithms to run efficiently against extremely large data in a distributed environment. With built-in support for bring-your-own-algorithms and frameworks, SageMaker offers flexible distributed training options that adjust to your specific workflows. Within a few steps, you can deploy a model into a secure and scalable environment from the SageMaker console.

Pricing for Amazon SageMaker

For information about Amazon Free Tier limits and the cost of using SageMaker, see Amazon SageMaker Pricing.

Are you a first-time user of Amazon SageMaker?

If you're a first-time user of SageMaker, we recommend that you complete the following:

  1. Overview of machine learning with Amazon SageMaker – Get an overview of the machine learning (ML) lifecycle and learn about solutions that are offered. This page explains key concepts and describes the core components involved in building AI solutions with SageMaker.

  2. Setting up Amazon SageMaker – Learn how to set up and use SageMaker based on your needs.

  3. Use automated ML, no-code, or low-code – Learn about low-code and no-code ML options that simplify a ML workflow by automating machine learning tasks. These options are helpful ML learning tools because they provide visibility into the code by generating notebooks for each of the automated ML tasks.

  4. Use machine learning environments offered by SageMaker – Familiarize yourself with the ML environments that you can use to develop your ML workflow, such as information and examples about ready-to-use and custom models.

  5. Explore other topics – Use the SageMaker Developer Guide's table of contents to explore more topics. For example, you can find information about ML lifecycle stages, in Overview of machine learning with Amazon SageMaker, and various solutions that SageMaker offers.

  6. Amazon SageMaker resources – Refer to the various developer resources that SageMaker offers.