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

Amazon SageMaker is a fully managed machine learning service. With 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, 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 few clicks from SageMaker Studio or the SageMaker console.

This guide includes information and tutorials on SageMaker features. For additional information, see Amazon SageMaker developer resources.


Amazon SageMaker Pricing

As with other Amazon products, there are no contracts or minimum commitments for using Amazon SageMaker. Training and hosting are billed by minutes of usage, with no minimum fees and no upfront commitments. For more information about the cost of using SageMaker, see SageMaker Pricing.

Are You a First-time User of Amazon SageMaker?

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

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

  2. Set Up Amazon SageMaker Prerequisites – This section explains how to set up your Amazon account.

  3. Amazon SageMaker Autopilot simplifies the machine learning experience by automating machine learning tasks. If you are new to SageMaker, it provides the easiest learning path. It also serves as an excellent ML learning tool that provides visibility into the code with notebooks generated for each of the automated ML tasks. For an introduction to its capabilities, see SageMaker Autopilot. To get started building, training, and deploying machine learning models, Autopilot provides:

  4. Get started – This section walks you through training your first model using SageMaker Studio, or the SageMaker console and the SageMaker API. You use training algorithms provided by SageMaker.

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

    • Submit Python code to train with deep learning frameworks – In SageMaker, you can use your own training scripts to train models. For information, see Machine Learning Frameworks and Languages.

    • Use SageMaker directly from Apache Spark – For information, see Use Apache Spark with Amazon SageMaker.

    • Use SageMaker to train and deploy your own custom algorithms – Package your custom algorithms with Docker so you can train and/or deploy them in SageMaker. To learn how SageMaker interacts with Docker containers, and for the SageMaker requirements for Docker images, see Use Docker containers to build models.

  6. View the API Reference – This section describes the SageMaker API operations.

How Amazon SageMaker Works

SageMaker is a fully managed service that enables you to quickly and easily integrate machine learning-based models into your applications. This section provides an overview of machine learning and explains how SageMaker works. If you are a first-time user of SageMaker, we recommend that you read the following sections in order: