A self-paced course on getting models into production and keeping them working. It covers six stages: what separates an ML engineer from a data scientist, data and feature engineering, training and evaluation that survives contact with reality, deployment patterns, monitoring and retraining, and the platform and cost work that makes the whole thing sustainable.

The emphasis is on the engineering around the model rather than the model itself, because that is where most production ML fails. Tech Skills Library is independent and issues no certification or credential.

Modules

Stage One — What an ML Engineer Actually Owns

Lessons

• The Gap Between a Notebook and a System Preview
• Problem Framing and Deciding Not to Use ML
• Reproducibility as the Foundation

Stage Two — Data and Feature Engineering

Lessons

• Feature Pipelines and the Training-Serving Gap
• Data Quality and Leakage
• Labels Imbalance and Sampling

Stage Three — Training and Evaluation

Lessons

• Model Selection Without Fashion
• Evaluation That Predicts Production Behaviour
• Tuning Experiment Discipline and Explainability

Stage Four — Deployment

Lessons

• Serving Patterns and Choosing Between Them
• Packaging Dependencies and Latency
• Rollout Shadow Mode and Rollback

Stage Five — Monitoring and Retraining

Lessons

• Drift and What It Actually Means
• Retraining Strategy
• Incident Response for Models

Stage Six — Platform Cost and Career

Lessons

• Building or Buying an ML Platform
• Cost Control in Training and Inference
• Building the Portfolio and Moving Into the Role