MLOps training
Master MLOps practices for deploying churn prediction models in production. Learn to create CI/CD pipelines, monitor model performance, handle incidents, ensure governance, and keep models accurate and aligned with business goals for reliable, scalable machine learning operations.

flexible workload from 4 to 360h
valid certificate in your country
What will I learn?
This MLOps course teaches you to build, deploy, and manage reliable churn prediction models end-to-end. Gain skills in feature engineering, model selection, evaluation, REST APIs, CI/CD pipelines, monitoring, incident response, retraining, and governance for scalable production systems.
Elevify advantages
Develop skills
- Develop production-ready churn models quickly through building, evaluation, and explanation.
- Master MLOps deployment using CI/CD, canary releases, and blue-green strategies safely.
- Implement monitoring and drift detection with metrics, logs, and alerts for ML health.
- Package models in containers and serve predictions via reliable REST APIs.
- Create incident response playbooks for rollbacks, outages, and performance issues.
Suggested summary
Before starting, you can change the chapters and workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
Your classes are perfect. I got the one-year package and finally have the chance to follow several topics I'm interested in without having to switch platforms... I appreciate everything you do, I've already recommended you to others...

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

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

Luciana AlvarengaNail Design Student
The platform is fast and easy to use. The variety of content and the extra videos really help with learning.

Giulio CarloPrompt Engineering Student
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