Google said today it’s bringing its Cloud AutoML service for training machine learning algorithms to Kaggle, its online community for data scientists.

Cloud AutoML is a cloud-based toolkit that provides a drag-and-drop interface for training AI models. Using the software, it’s possible to create customized machine learning models without any specific technical knowledge of the subject.

Kaggle is an online community for data scientists and other machine learning enthusiasts to explore, analyze and share their work. Google acquired the Kaggle platform in 2017, and the community recently hit a milestone when it surpassed 3.5 million members, Google said today.

By integrating Cloud AutoML with Kaggle, Google said it’s advancing its mission to “empower our community of data scientists by providing them with the skills and tools they need to lead in their field.”

Cloud AutoML works by allowing users to ingest data from a software development kit or web-based user interface, set up their parameters and then train a model based on that data which can later be deployed on Google’s cloud infrastructure. The new integration will enable Kaggle users to use the Cloud AutoML SDK within Jupyter Notebooks, which are open-source web applications used by data scientists to create and share documents that contain live code, equations, visualizations and narrative text.

“Today’s release focuses on enabling our community to use the SDK directly within Kaggle Notebooks,” Kaggle product manager Devvret Rishi said in a blog post.

Cloud AutoML is a paid service, though it does offer a free tier for beginners. To help sweeten the deal, Google is offering its users Google Cloud Platform credits throughout the year to help subsidize the cost of using AutoML with Kaggle.

The announcement follows a similar integration between Kaggle and Google’s BigQuery data warehouse service.

Photo: Google