Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML.
Introduction to Amazon SageMaker
Learn how to prepare, build, train, and deploy models with Amazon SageMaker.
Complete all the administrative tasks required to launch Amazon SageMaker Studio with just a few clicks.
In this tutorial, you use Amazon SageMaker Studio to build, train, deploy, and monitor an XGBoost model. We cover the entire machine learning (ML) workflow from feature engineering and model training to batch and live deployments for ML models.
Follow this step-by-step guide to start using all the features of Amazon SageMaker Studio.
In this on-demand tech talk, we show you how to quickly create new notebooks, upload data, train models, compare model results, and deploy models to production, all within Amazon SageMaker Studio.
Getting started with machine learning (ML) can be time-consuming. Amazon SageMaker JumpStart helps you quickly and easily get started with ML.
Learn to provision a secure ML environment, with a deep dive into common patterns and architectures required in regulated industries.
Secure and compliant ML workflows with Amazon SageMaker
Ever wondered how to build a secure and compliant end-to-end ML workflow for Financial Services? Check out this video demonstration, where we address the common patterns and requirements required by highly regulated industries for their use cases with secure machine learning.
Build machine learning models
Learn to build an ML model with the steps and resources outlined in this guide.
Access a rich repository of SageMaker notebooks, on GitHub.
Utilize algorithms built into Amazon SageMaker that are faster and cheaper than popular alternatives.
Amazon SageMaker offers many built-in algorithms that are optimized for speed, scale, and acuracy. Learn how to choose the right algorithm based on the problem you want to solve using ML.
R language is popular among data scientists and ML practitioners. In this video, learn how you can use R and run secure ML simulations at scale with Amazon SageMaker.
Learn how to set up containers using AWS services with ease and scale. This video will provide you with an understanding of how consistency and portability can be maintained in your ML development environment.
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
Learn how to use Amazon SageMaker Studio to train, and tune a TensorFlow deep learning model.
Read an overview of how to train machine learning models using Amazon SageMaker.
Organize and track your training iterations efficiently with Amazon SageMaker Experiments. Training an ML model typically entails many iterations to isolate and measure the impact of changing data sets, algorithm versions, and model parameters. SageMaker Experiments helps you manage these iterations by automatically capturing the input parameters, configurations, and results, and identify the best performing experiment.
Training an ML model typically entails many iterations to isolate and measure the impact of multiple variables. In this video, learn how Amazon SagMaker Experiments can help you and track these iterations within the visual interface of SageMaker Studio.
Try these examples of using hyperparameter tuning across different algorithms and deep learning frameworks.
Learn how to save up to 90% in training costs, using Amazon EC2 Spot instances with Managed Spot Training. Spot instances are space compute capacity and training jobs are automatically run when the spare capacity becomes available. Training runs are made resilient to interruptions caused by changes in capacity, allowing you to save cost when you have flexibility with when to run training jobs.
In this on-demand tech talk, learn how to use Amazon SageMaker Experiments and how Amazon SageMaker Debugger improves model quality through better model training and tuning. You will see how to manage iterations by automatically capturing the input parameters, configurations, and results and automatically capturing real-time metrics during training such as training and validation and confusion matrices.
The ML training process is largely opaque. Learn how Amazon SageMaker Debugger makes the training process transparent by automatically capturing metrics, analyzing training runs, and detecting problems.
Learn to train and tune your ML models to the highest accuracy with an in-depth video on training ML models with Amazon SageMaker.
Deploy machine learning models
Follow the step-by-step guide to deploy machine learning models on the highest performing 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 how Amazon SageMaker Multi-Model Endpoints enable a scalable and cost-effective way to deploy ML models at scale using a single end point.
AWS offers a breadth and depth of ML infrastructure with Amazon SageMaker. In this video, learn how to choose the proper compute instance for ML inference for your specific requirements.
ML Ops practices help data scientists and IT operations professional collaborate and manage the ML workflow. Learn how Amazon SageMaker can help you with ML Ops to easily manage and scale end-to-end workflows.
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.