Amazon SageMaker is one of the fastest growing services in the history of AWS, and today, tens of thousands of customers use SageMaker to build, train, and deploy high quality machine learning (ML) models. SageMaker brings together a broad set of capabilities purpose-built for ML including labeling, data preparation, feature engineering, statistical bias detection, auto-ML, training, tuning, hosting, explainability, monitoring, and workflows.
To help customers move faster on their ML projects, we are launching SageMaker Month where you can learn all about SageMaker including how to build, train, and deploy models faster. Take advantage of hands-on workshops, purpose-built tools, and getting started resources to improve team productivity by 10x. In addition, we are introducing several ways for you to save on SageMaker. First, you can save up to 64% using the new savings plans. Amazon SageMaker Savings Plans offer a flexible, usage-based pricing model in exchange for a commitment of consistent usage for a one or three year term. Plus, AWS reduced the price of several instance families by up to 14.2% so you can save even more.
Check out the SageMaker Month calendar to learn more.
SageMaker Savings Plan Overview (11:04)
Introducing the Amazon SageMaker Savings Plans, flexible plans to save up to 64% on ML usage costs
Machine learning for everyone with Domo and Amazon SageMaker Autopilot, bring together ML and BI
Learn how to use Amazon SageMaker Ground Truth to improve the quality of video annotations
Register to join the Amazon SageMaker fully-remote, day-long technical workshop
Easily prepare data for ML with Amazon SageMaker Data Wrangler
Build analytics in Tableau using models deployed on SageMaker
Manage and monitor models on edge devices such as wind turbines
Step-by-step guide to building predictive analytics
Annotate point cloud data using Amazon SageMaker Ground Truth
Meet one of LinkedIn's top AI voices, Greg Coquillo
Train and tune high quality deep learning models using SageMaker
Automate data labeling workflows with SageMaker Ground Truth
Deploy and manage models over time at scale with SageMaker
Tips for managing and customizing SageMaker Studio Notebooks
Learn how Genworth built a serverless ML pipeline on AWS using SageMaker
Get hands on building, training, and deploying models with SageMaker
Build a dashboard of data labels with SageMaker Ground Truth
Prepare data for predicting credit risk using SageMaker Data Wrangler and Clarify
Speed up YOLOv4 inference by 2x on SageMaker
It's a wrap, summary of SageMaker announcements