Automation of AI/ML in an industrial environment

Only 53% of the AI applications make it from a prototype stage to production” (Gartner). Companies often find it hard to create and maintain a stable development and production environment for AI and machine learning. For a robust MLOps process the following items are key: data collection is standardized, model training is automated and versioned, model performance is monitored continuously, and feedback is provided on explainable results with the human-in-the-loop. This talk highlights these items by using our automated visual inspection system as an example which we use to inspection our in-house designed and manufactured electronics.