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Machine Learning Runtime

Ready-to-use and optimized machine learning environment


The Machine Learning Runtime (MLR) provides data scientists and ML practitioners with scalable clusters that include popular frameworks, built-in AutoML and optimizations for unmatched performance.



Frameworks of Choice

ML Frameworks are evolving at a frenetic pace and practitioners need to manage on average 8 libraries. The ML Runtime provides one-click access to a reliable and performant distribution of the most popular ML frameworks, and custom ML environments via pre-built containers.


Augmented Machine Learning

Accelerate machine learning from data prep to inference with built-in AutoML capabilities including hyperparameter tuning and model search using Hyperopt and MLflow.


Simplified Scaling

Go from small to big data effortlessly with an auto-managed and scalable cluster infrastructure. The Machine Learning Runtime also includes unique performance improvements for the most popular algorithms as well as HorovodRunner, a simple API for distributed deep learning.


ML Frameworks: The most popular ML libraries and frameworks are provided out-of-the-box including TensorFlow, Keras, PyTorch, MLflow, Horovod, GraphFrames, scikit-learn, XGboost, numpy, MLeap, and Pandas.

How it works

The Machine Learning Runtime is built on top and updated with every Databricks Runtime release. It is generally available across all Databricks product offerings including: Azure Databricks, AWS cloud, GPU clusters and CPU clusters.

To use the ML Runtime, simply select the ML version of the runtime when you create your cluster.

Customer Story

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