Machine Learning Engineering
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Stage One — What an ML Engineer Actually Owns
The Gap Between a Notebook and a System
Preview
Problem Framing and Deciding Not to Use ML
Reproducibility as the Foundation
3 lessons
Stage Two — Data and Feature Engineering
Feature Pipelines and the Training-Serving Gap
Data Quality and Leakage
Labels Imbalance and Sampling
3 lessons
Stage Three — Training and Evaluation
Model Selection Without Fashion
Evaluation That Predicts Production Behaviour
Tuning Experiment Discipline and Explainability
3 lessons
Stage Four — Deployment
Serving Patterns and Choosing Between Them
Packaging Dependencies and Latency
Rollout Shadow Mode and Rollback
3 lessons
Stage Five — Monitoring and Retraining
Drift and What It Actually Means
Retraining Strategy
Incident Response for Models
3 lessons
Stage Six — Platform Cost and Career
Building or Buying an ML Platform
Cost Control in Training and Inference
Building the Portfolio and Moving Into the Role
3 lessons
Stage One — What an ML Engineer Actually Owns
Reproducibility as the Foundation
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