GCP: Demo of testing Airflow, MLflow, XGBoost
Training a machine-learning model on a laptop is the easy part. The hard part starts the day after: who reruns the training when new data arrives? Where are the experiments recorded? How does a better model reach production without someone copying files around, and how do you know it is actually better? Those questions are what MLOps is about. In this tutorial we will build, step by step, a small but complete MLOps platform on Google Cloud. The machine-learning model itself (an XGBoost classifier on the public UCI Bank Marketing dataset) is deliberately boring: it is only a vehicle. The real subject is everything around it: orchestrating, tracking, deploying, monitoring and reproducing an ML workflow. The context: what we are building and why Imagine a bank that wants to predict whether a customer will subscribe to a term deposit after a marketing call. A data scientist can train such a model in ten minutes. A team that has to retrain it every week, compare each new m...