ML Engineering & Deployment

• Part of: AI & Machine Learning — learning path 3 of 3
• Length: 3 chapters · 14 lessons · 8 hours a week
• Level: Intermediate

1. Python & Mathematics for ML4 chapters · 10 hours a week · 1 checkpoint
2. Machine Learning Fundamentals4 chapters · 12 hours a week · 1 checkpoint
3. ML Engineering & Deployment3 chapters · 8 hours a week · 1 checkpoint

Start this track

Syllabus

1. ML Project Structure & Experiment Tracking

• 5 lessons
• Skills: Experiment Tracking · MLflow · Weights & Biases · DVC · Project Structure

2. Model Serving & APIs

• 5 lessons
• Skills: FastAPI · Model Serving · Docker · Batch vs Online Inference

3. Monitoring, Drift & Retraining

• 4 lessons
• Skills: Model Monitoring · Data Drift Detection · Retraining Pipelines
• Checkpoint: MLOps Capstone Gate

Checkpoints

To pass each checkpoint you hand in a piece of real work. What each one asks for:

• MLOps Capstone Gate: Ship a trained model behind a FastAPI / Flask endpoint with input validation and structured logging. Add a drift-detection script that flags when incoming feature distributions diverge from training.