Databricks today introduced its AutoML Toolkit, an automated end-to-end machine learning service made to accommodate developers with a range of experience.

Available from Databricks Labs, the AutoML Toolkit can automate things like hyperparameter tuning, batch prediction, and model search.

AutoML Toolkit is built on existing Databricks tools like MLflow, an open source machine learning platform that integrates with frameworks like TensorFlow and Amazon SageMaker.

AutoML Toolkit executions are automatically tracked using MLflow.

The toolkit also utilities Apache Spark, an open source project created by Databricks founders and turned over to the Apache Spark Software Foundation in 2014.

The AutoML Toolkit differs from other AutoML solutions in that it allows data scientists and engineers with varying levels of expertise to work together, Databricks head of ML project management Clemens Mewald told VentureBeat in a phone interview.

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