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.
In this training course, learn how to use Amazon SageMaker to simplify the integration of machine learning into your applications. Key topics include: an overview of machine learning and problems it can help solve, using a Jupyter Notebook to train a model based on Amazon SageMaker’s built-in algorithms and, using Amazon SageMaker to publish the validated model. You will finish the course by building a serverless application that integrates with the Amazon SageMaker published endpoint.
In this training course, you will learn how to implement a machine learning pipeline using Amazon SageMaker and Amazon SageMaker Ground Truth. First you will create a labeled dataset, then you’ll create a training job to train your object detection model, and finally you will use Amazon SageMaker to create and update your model.
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.
In this video, learn all about the fully-managed notebook instances 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 video, learn about the high-performance algorithms, built-in with Amazon SageMaker.
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.
In this video, learn how to train and tune your machine learning models to the highest accuracy with Amazon SageMaker.
Identify your best ML models for your use case and get to production faster. Track, search, filter and sort your machine learning training runs using the steps outlined in this blog. You can now get to the best ML model across all your experiments using key model attributes, such as hyperparameter values and accuracy metrics, with Amazon SageMaker.
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.
In this video, learn how to deploy your ML models to production on the most scalable infrastructure.
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.
In this blog, learn how you can build, train, and deploy fastai models into Amazon SageMaker training and hosting by using the Amazon SageMaker Python SDK and a PyTorch base image. You can avoid the extra steps of building your own container.
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.