What Is Amazon SageMaker? - Amazon SageMaker

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. Training and hosting are billed by minutes of usage, with no minimum fees and no upfront commitments.

This guide includes information and tutorials on SageMaker features. To learn how to build, train, and deploy models using SageMaker, see Amazon SageMaker developer resources.

Amazon SageMaker Features

Amazon SageMaker includes the following features:

Amazon SageMaker Studio

An integrated machine learning environment 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 SageMaker notebooks that include SSO integration, fast start-up times, and single-click sharing.

Preprocessing

Analyze and pre-process data, tackle feature engineering, 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 alert 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 SageMaker, see Amazon 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 – This section explains how to set up your AWS account and onboard to SageMaker Studio.

  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 visiblity into the code with notebooks generated for each of the automated ML tasks. For an introduction to its capabilities, see Automate model development with Amazon SageMaker Autopilot. To get started building, training, and deploying machine learning models, Autopilot provides:

  4. Get Started with Amazon SageMaker – 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:

  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: