Automated Neural Network Model Training: The Impact on Deploying and Scaling ML at the Edge

Neural networks are being used to solve an ever-increasing number of use cases, elevating the importance of efficient model training and ongoing model maintenance. The value of a machine learning solution is easy to demonstrate in the lab but quickly diminishes when the end-user considers ongoing updates and the challenge of retraining models for changing real-world environments. This presentation will look at the benefits of using automated learning technologies with a federated gathering of training data from the outset so that models can self-tune and update, sometimes with zero-touch from the end-user. Often, by considering gathering additional insight at the edge, sufficient clues can be gathered to identify and auto-label training data. We’ll look at the impact on product rollout, configuration, maintenance and overall product effectiveness. We’ll end by looking at techniques for commoditization, bringing the required edge and cloud components together so that future products can more easily benefit from these technologies.