Still watching models die in a Jupyter notebook after the demo? Companies aren't hiring people who can train a model — they're hiring people who can keep one alive in production. This is the course that gets you there.
Learn Git-based versioning, MLflow, DVC, Docker, and CI/CD the way production ML teams actually use them — building towards a final project you can show, not just a certificate.
By the time you finish this course, you will have versioned a dataset with DVC, tracked and registered a model with MLflow, containerized it with Docker, and wired it into a CI/CD pipeline that redeploys automatically — the exact workflow that separates “I can build a model” from “I can ship one.” This course is how you get there in 8 weeks.
Syllabus
Phase 1: MLOps Lifecycle, Versioning & Tracking (Weeks 1–4)
Modules
Welcome to MLOps & Git for ML Projects
Reproducible Python Environments
Data Versioning with DVC
Experiment Tracking & Model Registry (MLflow)
Docker & CI/CD for ML I
Phase 1 Applied Lab
Phase 2: CI/CD, Monitoring & Final Project (Weeks 5–8)
Modules
CI/CD for ML II & Model Serving Basics
Basic Monitoring, Logging & Drift
Release Discipline
Final Project
Full session-by-session breakdown (all 24 sessions, 2-hour format) is in the downloadable curriculum PDF linked from the hero.
Outcome
By the end of this course, you will be able to:
- Version datasets and pipelines reproducibly with DVC
- Track experiments and manage a model registry with MLflow
- Containerize an ML model with Docker
- Build a CI/CD pipeline that tests, builds, and redeploys a model automatically
- Add basic monitoring and drift detection to a served model
- Apply release discipline: promotion criteria, rollback, audit trails
- Deliver a final project that operationalizes a real ML model end-to-end
- Career roles you'll be ready for: MLOps Engineer (Junior), ML Platform Engineer, DevOps Engineer transitioning into ML
Tools
Python
Git & GitHub
MLflow
Docker
GitHub Actions
FastAPI
Jupyter Notebook
Who Should Enrol
Complete beginner to MLOps?
Phase 1 starts with Git and reproducible Python from the ground up. No prior DevOps background required.
Data scientist who's only ever worked in notebooks?
Every concept is taught with a working lab, not slides — you'll leave with a model that's actually versioned, tracked, and deployed, not just a .ipynb file.
DevOps engineer moving into ML?
Your CI/CD and Git instincts transfer directly — Phase 1 moves fast on the DevOps side and focuses your time on what's ML-specific: DVC, MLflow, and drift.
Considering the full MLOps Engineering track?
This is Course 1 of 2. Finish this, then decide separately whether Production MLOps is your next step.
Market Growth
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FAQs
Data scientists, ML engineers, and DevOps professionals who want to learn how to reliably version, track, containerize, and deploy ML models — not just build them.
No. Phase 1 covers Git, Docker, and CI/CD fundamentals from the ground up. Basic Python and ML knowledge is assumed.
This course covers the core MLOps toolchain: versioning, tracking, containerization, and CI/CD. Production MLOps picks up from here and scales it to Kubernetes, observability, and incident response — this course is the prerequisite.
A final project that operationalizes a real ML model end-to-end: versioned data and code, tracked experiments, a model registry, a Dockerized serving API, and a CI/CD pipeline that redeploys it automatically.
Each course stands on its own with its own certificate. MLOps Foundations is a complete, job-ready skill set by itself — Production MLOps is there if you want to go further into scaling and operating ML in production.