ML Ops Course
This course teaches practical MLOps techniques to deploy, monitor, and maintain machine learning models in production environments, ensuring reliability, scalability, and stability.

4 to 360 hours flexible workload
valid certificate in your country
What will I learn?
This ML Ops Course shows you how to move models from notebooks to reliable production quickly and safely. You will learn canary and blue-green releases, fast rollback, CI/CD for models, containerization, Kubernetes serving, and secure configuration. The course also covers experiment tracking, versioning, monitoring, drift detection, and practical on-call runbooks so your ML systems stay stable and high performing.
Elevify advantages
Develop skills
- Production ML releases: ship models with canary, blue-green, and fast rollback.
- Reproducible ML training: version data, code, and models for audit-ready runs.
- Containerized ML serving: build lean Docker images and deploy to Kubernetes safely.
- CI/CD for ML models: automate tests, security scans, and zero-downtime rollouts.
- ML monitoring & on-call: detect drift, debug incidents, and run reliable churn models.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
Your lessons are perfect. I purchased the one-year package and finally have the opportunity to follow various topics of interest without needing to change platforms... I'm grateful for everything you do, I've already recommended you to other people...

Giulio CarloDigital Marketing Student
I like how the lessons are straight to the point and how I can change chapters and skip content I don't need.

Mariana FerresPhotography Student
I like the content and the way videos are presented and transcribed, which speeds up the process!

Luciana AlvarengaNail Design Student
The platform is fast and simple to use. The diversity of content and complementary videos really help with learning.

André FelipePrompt Engineering Student
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