Machine Learning Platforms often require hundreds or thousands of features, some of which can be irrelevant, redundant, or both. However, selecting the minimum number of optimal features from a large feature space in a Big Data DB is considered as an NP-complete problem.
Our proprietary MinOptDB Q© System (Minimal Optimal DataBase) is a low-energy, ML-augmented, and human-understandable technology that improves the performance and reduces the storage & computational cost of Machine Learning Platforms by: 1) eliminating the irrelevant and redundant features; 2) accelerating the model training and prediction speed; 3)reducing the monitoring and maintenance workload for the Big Data pipeline, and; 4) providing better model interpretation and diagnosis capability.
Our proprietary MinOptDB Q© beta is a Hybrid Quantum/Classical System that combines quantum state preparation and measurement with classical optimization on NISQ devices.
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