Amazon SageMaker
Developer Guide

What Is Amazon SageMaker?

Amazon SageMaker is a fully managed machine learning service. With Amazon SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so you don't have to manage servers. It also provides common machine learning algorithms that are optimized to run efficiently against extremely large data in a distributed environment. With native support for bring-your-own-algorithms and frameworks, Amazon SageMaker offers flexible distributed training options that adjust to your specific workflows. Deploy a model into a secure and scalable environment by launching it with a single click from the Amazon SageMaker console. Training and hosting are billed by minutes of usage, with no minimum fees and no upfront commitments.

This is a HIPAA Eligible Service. For more information about AWS, U.S. Health Insurance Portability and Accountability Act of 1996 (HIPAA), and using AWS services to process, store, and transmit protected health information (PHI), see HIPAA Overview.

Are You a First-time User of Amazon SageMaker?

If you are a first-time user of Amazon SageMaker, we recommend that you do the following:

  1. Read How It Works – This section provides an overview of Amazon SageMaker, explains key concepts, and describes the core components involved in building AI solutions with Amazon SageMaker. We recommend that you read this topic in the order presented.

  2. Read Getting Started – This section explains how to set up your account and create your first Amazon SageMaker notebook instance.

  3. Try a model training exercise – This exercise walks you through training your first model. You use training algorithms provided by Amazon SageMaker. For more information, see Step 3: Train a Model with a Built-in Algorithm and Deploy It.

  4. Explore other topics – Depending on your needs, do the following:

  5. See the API Reference – This section describes the Amazon SageMaker API operations.