Economical AutoML
Accelerating development of machine learning applications for engineers and data scientists
Economical AutoML is a project for automating the machine learning model development process such as hyperparameter optimization and learner selection for better model quality or faster inference using low computational resource.
More and more businesses start building millions of ML-embedded applications–it adds up to a large cost to manually choose the right training algorithm and tune the hyperparameters for every task and every dataset. Massive consumption of computation resources in tuning machine learning models also brings a tremendous burden to the environment.
For Python developers
We built FLAML (opens in new tab), a fast library for AutoML and tuning, based on our research. It finds high quality models at your fingertips. It is easy to customize and extend. It tunes fast and as you like.
For .NET developers
You can now access our technology from ML.NET Model Builder (opens in new tab) in Visual Studio 2022. It provides an easy-to-understand visual interface to build, train, and deploy custom machine learning models in Visual Studio. Prior machine learning expertise is not required.
You can also access our technology from ML.NET (opens in new tab) via code-first experience.
Read this Anyscale Blog (opens in new tab) to learn how it is integrated with Ray Tune to scale up distributed hyperparameter tuning.
