MLOps Foundations Course: Take ML Models to Production
Data scientists and backend developers wanting to move machine learning models from a notebook into a reliable, versioned, and monitored production pipeline.
Curriculum
- 1
Module 1: Experiment tracking & model versioning
Logging training runs, metrics, and artifacts with MLflow, and structuring a reproducible model registry.
- 2
Module 2: Packaging & serving models
Wrapping a trained model behind a FastAPI/BentoML inference service, containerizing it with Docker, and exposing a versioned REST/gRPC endpoint.
- 3
Module 3: CI/CD for ML pipelines
Automating retraining and evaluation gates in GitHub Actions, promoting models between staging and production registries.
- 4
Module 4: Monitoring model drift
Tracking prediction quality, data drift, and latency in production, and setting up alerts before a model silently degrades.
Who is this course for?
Data scientists and backend developers wanting to move machine learning models from a notebook into a reliable, versioned, and monitored production pipeline.
What you will build
A containerized model-serving API with MLflow-tracked experiments, an automated retraining pipeline, and basic drift monitoring in production.
Frequently asked questions
How long is the MLOps Foundations Course: Take ML Models to Production?
The course runs 4 sessions (8 hours), taught 1:1 with a mentor on a flexible schedule.
Who is this course for?
Data scientists and backend developers wanting to move machine learning models from a notebook into a reliable, versioned, and monitored production pipeline.
What will I have built by the end?
A containerized model-serving API with MLflow-tracked experiments, an automated retraining pipeline, and basic drift monitoring in production.
How much does the course cost?
Tuition is quoted based on your personalised learning path. Contact us on Zalo or WhatsApp for advice and a detailed quote.
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Message the mentor for a learning path that fits your level, goals and schedule. Tuition is quoted per personalised path.
