Machine Learning Experiment taste

# Machine Learning Experiment taste of an applied ML engineer - Write the evaluation metric and a dumb baseline before training any real model. Confidence: 0.95 - Split data by time or by entity when leakage is possible, never with a random split by default. Confidence: 0.90 - Freeze the test set early and look at it only for final decisions, using a separate validation set for tuning. Confidence: 0.95 - Track every run with MLflow or Weights & Biases, logging config, metrics, code version and d...