What the course covers.
- Module 1
MLOps Fundamentals & Workflow
Learners begin by exploring the principles of MLOps, including the AI/ML lifecycle, version control, experiment tracking, and reproducible workflows. Hands-on exercises demonstrate how to structure projects for scalability and maintainability, bridging the gap between model development and production deployment.
- Module 2
Deployment & Continuous Integration
This module covers practical deployment techniques, including creating APIs for ML models, containerisation with Docker, orchestration with Kubernetes, and CI/CD pipelines. Learners gain experience automating model deployment, testing, and updating, ensuring AI systems are robust, repeatable, and easy to manage.
- Module 3
Monitoring, Scaling & Production Best Practices
Learners focus on monitoring model performance, detecting drift, handling data updates, and scaling AI systems efficiently. The module also addresses operational challenges such as latency, reliability, and fault tolerance. By applying best practices, participants learn how to keep production AI systems performant and maintainable over time.
