Amazon announced half a dozen new features and tools for AWS SageMaker

Amazon today announced half a dozen new features and tools for AWS SageMaker, a toolkit for training and deploying machine learning models to help developers better manage projects, experiments, and model accuracy.

AWS SageMaker Studio is a model training and workflow management tool that collects all the code, notebooks, and project folders for machine learning into one place, while SageMaker Notebooks lets you quickly spin up a Jupyter notebook for machine learning projects. CPU usage with SageMaker Notebooks can be managed by AWS and quickly transfer content from notebooks.

There’s also SageMaker Autopilot, which automates the creation of machine learning models and automatically chooses algorithms and tunes models.

“With AutoML, here’s what happens: You send us your CSV file with the data that you want a model for where you can just point to the S3 location and Autopilot does all the transformation of the model to put in a format so we can do machine learning; it selects the right algorithm, and then it trains 50 unique models with a little bit different configurations of the various variables because you don’t know which ones are going to lead to the highest accuracy,” CEO Andy Jassy said onstage today at re:Invent in Las Vegas. “Then what we do is we we give you in SageMaker Topplay Studio a model leaderboard where you can see all 50 models ranked in order of accuracy. And we give you a notebook underneath every single one of these models, so that when you open the notebook, it has all the recipe of that particular model.”

SageMaker Experiments is for training and tuning models automatically and capture parameters when testing models. Older experiments can be searched for by name, data set use, or parameters to make it easier to share and search models.