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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 are required to simplify and automate machine learning and other data science processes and techniques – to enable data scientists to spend less time on administration and more time on high value solutions for complex problems.