Why Model Explainability is The Next Data Science Superpower

I've interviewed many data scientists in the last 10 years, and model explainability techniques are my favorite topic to distinguish the very best data scientists from the average. Some people think machine learning models are black boxes, useful for making predictions but otherwise unintelligible; but the best data scientists know techniques to extract real-world insights from any model. Answering these questions is more broadly useful than many people realize. This inspired me to create Kaggle's model explainability micro-course. Whether you learn the techniques from Kaggle or from a comprehensive resource like Elements of Statistical Learning, these techniques will totally change how you build, validate and deploy machine learning models.