A prompt can be three paragraphs long and still change the behavior of an entire application. It can decide whether an assistant answers, abstains, calls a tool, or invents the argument that the tool receives. Yet it is often reviewed with less discipline than a change to a configuration file. Let’s start there. If editing a sentence can change what your software does, that sentence belongs in your release process. ...
Complete MLOps Series: ← Part 1: Pipeline | ← Part 2: Deployment | Part 3 (current) Anatomy of an MLOps Pipeline - Part 3: Production and Best Practices 11. Model and Parameter Selection Strategies The Complete Flow: Selection → Sweep → Registration This pipeline implements a three-phase strategy for model optimization, each with a specific purpose: Step 05: Model Selection ├── Compares 5 algorithms with basic GridSearch (5-10 combos/model) ├── Objective: Identify best model family (Random Forest vs Gradient Boosting vs ...) ├── Primary metric: MAPE (Mean Absolute Percentage Error) └── Output: Best algorithm + initial parameters Step 06: Hyperparameter Sweep ├── Optimizes ONLY the best algorithm from Step 05 ├── Bayesian optimization with 50+ runs (exhaustive search space) ├── Objective: Find optimal configuration of best model ├── Primary metric: wMAPE (Weighted MAPE, less biased) └── Output: best_params.yaml with optimal hyperparameters Step 07: Model Registration ├── Trains final model with parameters from Step 06 ├── Registers in MLflow Model Registry with rich metadata ├── Transitions to stage (Staging/Production) └── Output: Versioned model ready for deployment Why three separate steps? You don’t have computational resources to do exhaustive sweep of 5 algorithms × 50 combinations = 250 training runs. First decide strategy (which algorithm), then tactics (which hyperparameters). ...