Machine learning models are very different from code. When you deploy code you don't really need to monitor it on how it is delivering the results. However, ML models are different, we need to monitor their input data and measure them to a baseline. This is what we talk about in todays episode and talk on Services like AWS Sagemaker, Model Monitor, Model Drift and data collection. The process of Model Monitor is part of the MLOps lifecycle