Course · Interview foundations
MLOps for Data Engineers
Free MLOps course: the ML lifecycle, experiment tracking and model registries, batch and online serving, and monitoring for drift, explained with runnable code.
- Lessons
- 4
- Interview questions
- 0
- Projects & case studies
- 0
- Reading time
- ~1 h
Your progress
Saved in this browser onlyCourse structure
- 1 lessonStart hereThe complete overview of the course in one read.
- 3 lessonsIntermediatePatterns used in production pipelines.
Practise
Lessons
Work through the lessons in order. Completed lessons show a tick; lessons you have opened are outlined.
Start here
The complete overview of the course in one read.
Intermediate
Patterns used in production pipelines.
- Experiment Tracking and Model Registries, with PythonHow to track ML experiments and register models: save metrics, settings and data fingerprints with each version, and promote models by rule, with Python.
- Model Serving: Batch vs Online PredictionsHow to serve ML predictions: batch scoring vs online APIs, input validation, latency and versioning, with a runnable batch job and a small prediction API.
- Model Monitoring and Data Drift: Keeping Models HealthyHow to monitor ML models in production: data drift, concept drift, the population stability index (PSI), alerts and when to retrain, with runnable Python.
Resources
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