Model Training | Anyscale

Scalable Model Training

There are four common patterns of machine learning production: pipeline, ensemble, business logic, and online learning. Implementing these patterns typically involves a tradeoff between ease of development and production readiness. Web frameworks are simple and work out of the box but can only provide single predictions; they cannot deliver performance or scale. Custom tooling glue tools together but are hard to develop, deploy, and manage. Specialized systems are great at training ML models but they are not as flexible or easy to use and can be costly.

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Anyscale helps you go beyond existing model training limitations with Ray and Ray Train, which offers scalable, efficient, composable, and flexible ML training. Ray Train provides:

Framework support for XGBoost, PyTorch, Tensorflow & more

A build specific for ML practitioners with standard ML tools and features

Seamless operation in the Ray ecosystem as part of Ray AIR

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Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud — with no changes.

Iterate & Move to Production Fast With Ray Train & 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.

What You Need for the Right Scalable ML Platform

An Open, Broad Ecosystem

Gain access to the most up-to-date technologies and their communities. 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.

Emiliano Castro, Principal Data Scientist, WildLife

"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."

Greg Brockman, Co-founder, Chairman, & President, OpenAI

"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."

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Explore how thousands of engineers from companies of all sizes and across all verticals are tackling real-world workloads with Ray and Anyscale.

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