2Machine learning model lifecycle and operations

• 2.1Orchestrating model training

  Understand experiment tracking with MLflow, automated machine learning (AutoML), notebook exploration, hyperparameter tuning (sweeps), running training scripts, distributed training for large/deep models, training pipelines, and comparing model performance across jobs.

• 2.2Model registration, versioning, and responsible AI evaluation

  Understand registering an MLflow model, packaging a feature retrieval specification with the model artifact, evaluating a model using responsible AI principles, and managing the model lifecycle including archiving.

• 2.3Production deployment and monitoring

  Understand deploying models as real-time/batch managed endpoints, testing and troubleshooting endpoints, progressive rollout and safe rollback, and in production configuring data drift detection, performance-metric monitoring, and retraining/alert triggers when thresholds are exceeded.