Production ML Engineer
Production ML Engineers bridge the gap between model development and reliable real-world deployment. Their English usage spans writing model cards, communicating drift alerts to stakeholders, and designing A/B experiments with data scientists. This path covers the vocabulary and communication patterns needed to operate machine-learning systems safely and transparently at scale.
Topics covered
- Feature Stores & Data Pipelines
- Model Serving & Inference
- A/B Testing ML Models
- Drift & Performance Monitoring
- Model Cards & Documentation
- ML System Design
Vocabulary spotlight
4 terms every Production ML Engineer should know in English:
A centralised repository that stores, manages, and serves curated features for model training and inference
"The feature store ensures that training and serving use identical feature transformations."
A change in the statistical distribution of model input data over time that can degrade model performance
"Data drift was detected in the user-age feature after a marketing campaign shifted the audience demographics."
A deployment strategy in which a new model receives live traffic and generates predictions that are logged but not served to users
"We ran the new recommendation model in shadow mode for two weeks before promoting it to production."
A short document that discloses the intended use, performance characteristics, and limitations of a machine-learning model
"The model card describes the training data, evaluation metrics, and known failure modes for the fraud-detection model."
📚 Vocabulary Reference
Key terms organised by category for Production ML Engineers:
Feature Engineering & Stores
Model Serving & Deployment
Monitoring & Reliability
Governance & Documentation
Recommended exercises
Real-world scenarios you'll practise
- Writing a model card for a production classifier ahead of a compliance review.
- Presenting A/B test results and statistical significance to a non-technical product audience.
- Communicating a data-drift incident and rollback decision to engineering and business stakeholders.
- Designing a feature-store schema and explaining the rationale in a system-design document.