AWS launches major SageMaker upgrades for machine learning model training and testing

AWS launches major SageMaker upgrades for machine learning model training and testing

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.

Above: Amazon SageMaker Studio screenshot

Image Credit: Amazon

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 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.

SageMaker Debugger is made to improve accuracy of machine learning models, while SageMaker Model Monitor is a way to detect concept drift.

“With concept drift, what we do is we create a set of baseline statistics on the data in which you train the model and then we actually analyze all the predictions, compare it to the data used to create the model, and then we give you a way to visualize where there appears to be concept drift, which you can see in SageMaker Studio,” Jassy said.

Machine learning frameworks like PyTorch and TensorFlow have seen more adoption than SageMaker, but 85% of TensorFlow use in the cloud today happens with AWS, Jassy said.

The series of new tools were introduced today alongside a range of machine learning cloud services for people without machine learning expertise like Kendra, Fraud Detector, and Inf1, an instance for AI inference. AWS also today debuted Graviton2, a chip for datacenters due out next year.

SageMaker made its debut at re:Invent in 2017. At re:Invent last year, SageMaker got an upgrade with automated data-labeling service SageMaker Ground Truth and SageMaker RL for reinforcement learning.

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