MLOps training
Master MLOps for building reliable churn prediction systems. Learn end-to-end deployment, CI/CD pipelines, monitoring, incident response, model governance, and observability to deliver scalable ML products that meet business KPIs and perform reliably in production environments.
from 4 to 360h flexible workload
certificate valid in your country
What will I learn?
This MLOps course teaches you to design, package, deploy, and monitor robust churn prediction models end-to-end. Gain hands-on skills in feature engineering, model selection, evaluation, REST APIs, CI/CD pipelines, automated validation, deployment patterns, observability, incident runbooks, retraining, and governance for accurate, scalable, production-ready models.
Elevify advantages
Develop skills
- Production-ready churn modelling: build, evaluate, and explain churn models quickly.
- MLOps deployment expertise: deploy ML models safely using CI/CD, canary, and blue-green strategies.
- Monitoring and drift detection: monitor ML system health with metrics, logs, and alerts.
- Model packaging and APIs: containerise models and deliver predictions via REST APIs.
- Incident response playbooks: manage rollbacks, outages, and performance issues effectively.
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, simple to use. The diversity of content and complementary videos really help with learning.
André FelipePrompt Engineering Student
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