Amazon SageMaker is a fully managed, modular service that helps developers and data scientists to build, train, and deploy machine learning models at scale. Get started with these developer resources, so you can quickly move from concept to production.
Introduction to Amazon SageMaker
Learn about the build, train, and deploy modules of Amazon SageMaker.
Build machine learning models
Use the Build module of SageMaker to collect and prepare training data, access pre-built notebooks, and leverage the built-in, high performance algorithms.
Learn to build an ML model with the steps and resources outlined in this guide.
Follow this guide to start using notebook instances that are fully managed and pre-built with Amazon SageMaker.
Read this blog and learn to use common workflows using Amazon SageMaker notebook instances.
Acccess a rich repository of SageMaker notebooks, on GitHub.
Utilize algorithms built into Amazon SageMaker that are faster and cheaper than popular alternatives.
In this on-demand tech talk, learn to build intelligent applications using Amazon SageMaker and other AWS services.
Train and tune machine learning models
Use the Train module to set up training environments with one click and optimize your model using automatic module tuning
Read an overview of how to train machine learning models using Amazon SageMaker.
Learn how to incrementally train a machine learning model saving time and resources.
Follow detailed steps to tune your model to the highest accuracy using automatic model tuning. You can find the best version of your model by automatically running many training jobs using your chosen algorithms and ranges of hyperparameters for those algorithms.
Try these examples of using hyperparameter tuning across different algorithms and deep learning frameworks.
Learn how to automatically tune the hyperparameter values of the algorithm in your machine learning model to obtain the most accurate predictions.
In this on-demand tech talk, learn to train TensorFlow-based machine learning models. Understand the unique combination of TensorFlow and Amazon SageMaker to accelerate training of your machine learning models and bring them to production.
Deploy machine learning models
Use the Deploy module to deploy your machine learning models to production with a single click.
Follow the step-by-step guide to deploy machine learning models on the highest performing infrastructure.
Understand the best practices to deploy machine learning models at scale using Amazon SageMaker.
Follow the examples on GitHub to use Amazon SageMaker and AWS Step Functions to automate the building, training, and deploying of custom machine learning models.
Learn to use the deployment capabilities of SageMaker including A/B testing and Auto Scaling, delivering high performance and high availability for your machine learning models.
In this on-demand tech talk, learn about the machine learning life cycle, best practices for using Amazon SageMaker in your enterprise, and how to integrate Amazon SageMaker with other AWS services.
Use APIs tailored to your programming language or platform to make it easy to use Amazon SageMaker in your applications.
What’s New announcements are high-level summaries of launches and feature updates. Read Amazon SageMaker specific updates and other AWS announcements.