Model Development
Building and iterating on ML models in an MLOps context.
Chapter 9: Model Training Pipelines Pipeline orchestration: Airflow, Kubeflow Pipelines Distributed training with Horovod Chapter 10: Hyperparameter Optimization Grid search, random search Bayesian optimization Tools: Optuna, Ray Tune Chapter 11: Experiment Tracking Tools: MLflow, Weights & Biases, TensorBoard Metrics logging Artifact storage Chapter 12: Model Evaluation Cross-validation A/B testing Fairness metrics Tools: Scikit-learn, Fairlearn