Most ML projects stall between "model works in a notebook" and "model runs reliably in production" — this course is specifically about closing that gap.

What You’ll Learn

Model versioning and experiment tracking
CI/CD pipelines for ML
Model serving patterns (batch and real-time)
Monitoring for drift and performance degradation

Curriculum

MLOps Landscape & Tooling
Experiment Tracking & Versioning
CI/CD for ML Pipelines
Model Serving Patterns
Monitoring & Drift Detection
Capstone: Deploy a Model to Production

Who Should Attend

ML engineers and data scientists moving models into production.

Prerequisites

Machine learning fundamentals; basic DevOps concepts helpful.

Hands-on Labs

Practice what you learn.

Hands-on lab availability is shown in the course details and enrolment information.

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Related Courses

Keep going after this course.

Part Of A Larger Path

This course is one stage in the AI & Machine Learning Learning Path

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Ready to Start

Talk to an advisor about MLOps & AI Model Deployment

Cohort dates, corporate training options, or how this fits your learning path.

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