TUPAQ - Automating Model Search for Large Scale Machine Learning

 

 
Simplifying and automating machine learning processes and techniques - that depend on large-scale, distributed datasets to achieve high statistical performance - is critical for the future of applied data science. TUPAQ is a new architecture for automating machine learning comprised of a cost-based cluster resource allocation estimator, advanced hyperparameter tuning techniques, bandit resource allocation via runtime algorithm introspection, and physical optimization via batching and optimal resource allocation. 
 
TUPAQ finds and trains models for a user’s predictive application and scales to models trained on Terabytes of data across hundreds of machines. 
 
In the future innovative tools for data scientists are required to simplify and automate machine learning and other data science processes and techniques.