Fundamentals of AI and ML
ML Lifecycle
The ML lifecycle covers the repeated path from business framing through monitoring. The exam expects you to know that deployment is not the end; drift, quality, latency, and business outcomes need ongoing review.
Concepts
- Typical steps include business framing, data collection, data preparation, feature engineering, training, evaluation, deployment, monitoring, and iteration.
- Feature engineering transforms raw data into useful model inputs.
- Monitoring watches for data drift, model drift, quality regressions, latency, and operational issues.
Exam tips
- Feature engineering turns raw data into useful model inputs.
- Monitoring can detect data drift, model drift, quality changes, and operational issues.
- Iteration is normal when data, requirements, or model behavior changes.