The latest BigML release has brought OptiML to our platform, and it is now available from the BigML Dashboard, API, and WhizzML. This new resource automates Machine Learning model optimization for all knowledge workers to lower even more the barriers for everyone to adopt Machine Learning.
OptiML is an optimization process for model selection and parametrization that automatically finds the best supervised model to help you solve classification and regression problems. OptiML creates and evaluates hundreds of supervised models (decision trees, ensembles, logistic regression, and deepnets) with multiple configurations to finally return a list of the best models for your data, so it saves practitioners significant time in exploring hypothesis spaces by preventing exhaustive trial and error experimentation with different algorithms and their parameter configurations. All these details and more are explained in the video webinar, released yesterday during the official launch, and available on the BigML YouTube channel.
For further learning on OptiML, please visit our release page, where you will find:
- The slides used during the webinar.
- The detailed documentation to learn how to use OptiML from the BigML Dashboard and the BigML API.
- The series of six blog posts that gradually explain OptiML.
Thanks for your support and great feedback! Feel free to reach out to the BigML Team at firstname.lastname@example.org anytime. Your suggestions and questions are always welcome!