How to make sure AI and ML models survive and thrive

An urban legend says that a data science task is mainly finished after the development of models. The truth is that a much more important phase follows, often tougher than model development: managing and governing these ready-to-use models to keep your data science project relevant for the long haul. If you're a visual learner, you might prefer tuning into my Open Data Science Conference presentation, First Aid Kit for Data Science: Keeping Machine Learning Alive. In just over 24 minutes, I cover the machine-learning lifecycle, which includes finding the right data, preparing and exploring it and building, registering and reassigning models. I use a fraud detection project as an example.