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.

Amazon SageMaker Features

Amazon SageMaker includes the following features:

Amazon SageMaker Studio

An integrated machine learning enviroment where you can build, train, deploy, and analyze your models all in the same application.

Amazon SageMaker Ground Truth

High-quality training datasets by using workers along with machine learning to create labeled datasets.

Amazon Augmented AI

Human-in-the-loop reviews

Amazon SageMaker Studio Notebooks

The next generation of Amazon SageMaker notebooks that include SSO integration, fast start-up times, and single-click sharing.


Analyze and pre-process data, tackle feature egineering, and evaluate models.

Amazon SageMaker Experiments

Experiment management and tracking. You can use the tracked data to reconstruct an experiment, incrementally build on experiments conducted by peers, and trace model lineage for compliance and audit verifications.

Amazon SageMaker Debugger

Inspect training parameters and data throughout the training process. Automatically detect and alerts users to commonly occurring errors such as parameter values getting too large or small.

Amazon SageMaker Autopilot

Users without machine learning knowledge can quickly build classification and regression models.

Reinforcement Learning

Maximize the long-term reward that an agent receives as a result of its actions.

Batch Transform

Preprocess datasets, run inference when you don't need a persistent endpoint, and associate input records with inferences to assist the interpretation of results.

Amazon SageMaker Model Monitor

Monitor and analyze models in production (endpoints) to detect data drift and deviations in model quality.

Amazon SageMaker Neo

Train machine learning models once, then run anywhere in the cloud and at the edge.

Amazon SageMaker Elastic Inference

Speed up the throughput and decrease the latency of getting real-time inferences.

Amazon SageMaker Pricing

As with other AWS products, there are no contracts or minimum commitments for using Amazon SageMaker. For more information about the cost of using Amazon SageMaker, see Amazon SageMaker Pricing.

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 Amazon SageMaker 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 Get Started with Amazon SageMaker – 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 Get Started with Amazon SageMaker.

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

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