Extreme Hyper Tuning

Big Data and Big Models have ‘hyper’ parameters and settings that dramatically affect their usefulness.  A model that works ok today, needs to work even better tomorrow. 

Continuous, efficient, hypertuning helps ensure you maintain the best approach and settings to get maximum value.  

Traditional grid search, random search, open source or favorite settings approaches are not good enough.  These all waste time and critical resources and leave opportunity on the table.

MagicOpt’s hypertuning instantly enhances your optimization process to accelerate gains and dramatically improve the use of time and computing resources.

MagicOpt hypertuning is flexible and easy-to-use

A command line tool guides the process and calls your function (through a simple wrapper).

Our python module can drive the process or can be called for “next settings groups” to try by your code.

Our easy to use REST API allows you to take complete control, making integration possible with any pipeline.