Scalable ML
Platforms on Ray

Anyscale and Ray provide the tools you need to manage compute for data processing, machine learning training, and model deployment.

Build the future with scalable ML platforms

Many organizations now have machine learning experts and teams building infrastructure, tools, and abstracted layers so that developers and data scientists can iterate and execute simply and effectively on their machine learning (ML) workloads.

But with the variety of ML use, both internal and external, comes the difficulty of building ML platforms that can meet the needs of different and sometimes conflicting requirements.

What’s needed are flexible and scalable machine learning platforms that can handle disparate inputs, data types, dependencies, and integrations.

What you need for the right scalable ML platform

Train, test, deploy, serve, and monitor machine learning models efficiently and with speed with Ray and Anyscale.

  • Scale with a click

    Rely on a robust infrastructure that can scale up machine learning workflows as needed. Scale everything from XGBoost to Python to TensorFlow to Scikit-learn on top of Ray.

  • An open, broad ecosystem

    Gain to the most up-to-date technologies and their communities, don’t limit what libraries or packages you can use for your models. Load data from Snowflake, Databricks, or S3. Track your experiments with Weights & Balances or MLFlow. Or monitor your production services with Grafana. Don’t limit yourself.

  • Iterate quickly

    Reduce friction and increase productivity by eliminating the gap between prototyping and production. Use the same tech stack regardless of environment.


“Ray and Anyscale have been instrumental in scaling Dendra Systems’ machine learning platform to handle our ever increasing dataset of ultra-high resolution UAV imagery.”

Shuning Bian

Shuning Bian

Chief Architect

The magic of Merlin

Learn how Shopify granted its machine learning platform Merlin the magic to help data scientists and ML engineers streamline their machine learning workflows.

See how other others are creating their scalable ML platforms


At OpenAI, we are tackling some of the world’s most complex and demanding computational problems. Ray powers our solutions to the thorniest of these problems and allows us to iterate at scale much faster than we could before. As an example, we use Ray to train our largest models, including ChatGPT.

Greg Brockman

Co-founder, Chairman, and President, OpenAI


Ray and Anyscale empower even the leanest teams to bring AI to production and realize the business potential of AI in record time.

Laure Fouilloux

Head of Data Intelligence, Ricardo


We chose Ray as the unified compute backend for our machine learning and deep learning platform because it has allowed us to significantly improve performance and fault tolerance, while also reducing the complexity of our technology stack. Ray has brought significant value to our business.

Xu Ning

Senior Manager, Uber AI Platform


Being able to operate quickly at this massive scale has enabled us to deliver novel solutions towards Dendra Systems’ mission of scaling ecosystem restoration of our biodiverse natural world.

Shuning Bian

Chief Architect, Dendra Systems


Ray and Anyscale have enabled us to quickly develop, test and deploy a new in-game offer recommendation engine based on reinforcement learning, and subsequently serve those offers 3X faster in production. This resulted in revenue lift and a better gaming experience.

Emiliano Castro

Principal Data Scientist


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