AWS News Blog
Category: Amazon SageMaker
Amazon SageMaker Studio: The First Fully Integrated Development Environment For Machine Learning
Today, we’re extremely happy to launch Amazon SageMaker Studio, the first fully integrated development environment (IDE) for machine learning (ML). We have come a long way since we launched Amazon SageMaker in 2017, and it is shown in the growing number of customers using the service. However, the ML development workflow is still very iterative, […]
Amazon SageMaker Debugger – Debug Your Machine Learning Models
Today, we’re extremely happy to announce Amazon SageMaker Debugger, a new capability of Amazon SageMaker that automatically identifies complex issues developing in machine learning (ML) training jobs. Building and training ML models is a mix of science and craft (some would even say witchcraft). From collecting and preparing data sets to experimenting with different algorithms […]
Amazon SageMaker Model Monitor – Fully Managed Automatic Monitoring For Your Machine Learning Models
Today, we’re extremely happy to announce Amazon SageMaker Model Monitor, a new capability of Amazon SageMaker that automatically monitors machine learning (ML) models in production, and alerts you when data quality issues appear. The first thing I learned when I started working with data is that there is no such thing as paying too much […]
Amazon SageMaker Processing – Fully Managed Data Processing and Model Evaluation
Today, we’re extremely happy to launch Amazon SageMaker Processing, a new capability of Amazon SageMaker that lets you easily run your preprocessing, postprocessing and model evaluation workloads on fully managed infrastructure. Training an accurate machine learning (ML) model requires many different steps, but none is potentially more important than preprocessing your data set, e.g.: Converting […]
Amazon SageMaker Autopilot – Automatically Create High-Quality Machine Learning Models With Full Control And Visibility
Update September 30, 2021 – This post has been edited to remove broken links. Today, we’re extremely happy to launch Amazon SageMaker Autopilot to automatically create the best classification and regression machine learning models, while allowing full control and visibility. In 1959, Arthur Samuel defined machine learning as the ability for computers to learn without being […]
Amazon SageMaker Experiments – Organize, Track And Compare Your Machine Learning Trainings
Today, we’re extremely happy to announce Amazon SageMaker Experiments, a new capability of Amazon SageMaker that lets you organize, track, compare and evaluate machine learning (ML) experiments and model versions. ML is a highly iterative process. During the course of a single project, data scientists and ML engineers routinely train thousands of different models in […]
Now Available on Amazon SageMaker: The Deep Graph Library
Today, we’re happy to announce that the Deep Graph Library, an open source library built for easy implementation of graph neural networks, is now available on Amazon SageMaker. In recent years, Deep learning has taken the world by storm thanks to its uncanny ability to extract elaborate patterns from complex data, such as free-form text, […]
New for Amazon Aurora – Use Machine Learning Directly From Your Databases
March 23, 2020: Post updated to clarify networking, IAM permissions, and database configurations required to use machine learning from Aurora databases. A new notebook using SageMaker Autopilot gives a complete example, from the set up of the model to the creation of the SQL function using the endpoint. The integrations described in this post are now available for MySQL and […]