What ML Engineers actually do, the full lifecycle, model drift, and MLOps. Four concept pages, no code.
Evaluation metrics, feature engineering, training fundamentals, experiment tracking. Concept pages plus problems.
Model serving, deployment patterns, monitoring, distributed training. Medium problems with real production scenarios.
Model compression, A/B testing for ML, feature stores at scale. Hard problems with golden-answer comparison.
Company-tagged problems only. Required 15-minute simulator capstone. Completion generates a shareable certificate.