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

    Have access 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.”

Feedback by Shuning Bian

Shuning Bian

Chief Architect

The magic of Merlin

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

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