In this talk, we will introduce a novel platform Resource-Aware AutoML (RA-AutoML) which enables flexible and generalized algorithms to build machine learning models subjected to resource and hardware constraints. RA-AutoML intelligently conducts Hyper-Parameter Search (HPS) as well as Neural Architecture Search (NAS) to build models optimizing predefined objectives.
RA-AutoML is a versatile framework that allows human user to prescribe many resource/hardware constraints and objectives demanded by the problem or business requirements. At its heart, RA-AutoML relies on our in-house search-engine algorithm, MOBOGA, which combines a modified constraint-aware Bayesian optimization and Genetic algorithm to construct Pareto optimal candidates. Our experiments on CIFAR-10 dataset shows very good accuracy compared to results obtained by state-of-art neural network models, while subjected to resource constraints in the form of model size.
A research data scientist at Accenture Labs in San Francisco
A R&D senior analyst at Accenture Labs in San Francisco
A R&D data scientist at Accenture Labs in San Francisco