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 and federated learning technologies from the outset so that models can self-tune and update, sometimes with zero-touch from the end-user. Often, by recognizing human actions, 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.