MLOps and Cloud Engineering

Course Code: Y2D1
ECTS Credits: 5.0

Course Description

This course introduces students to the principles and practices of deploying machine learning solutions in production environments. Students learn how to structure, automate, monitor, and manage ML systems at scale using modern MLOps tools and frameworks.

By working through deployment scenarios, students gain hands-on experience with containerization, cloud computing, model serving, and continuous integration/continuous deployment (CI/CD) pipelines. Emphasis is placed on reproducibility, scalability, and maintainability.

Course Content

  • MLOps Concepts and Lifecycle
  • Reproducible Experiments with MLflow and Azure ML
  • Continuous Integration / Deployment (CI/CD) with GitHub Actions
  • Model Deployment Options: Real-Time vs Batch
  • Containerization with Docker
  • Using Azure Machine Learning Services
  • Monitoring, Logging, and Retraining Strategies
  • Version Control of Code, Data, and Models

Prerequisites

  • This course builds on earlier work in which students developed machine learning models for natural language processing (Y2A1) or computer vision (Y2B1). These models will now be prepared for production deployment.

  • Completion of all Year 1 courses in the Applied Data Science & AI programme.

Course Coordinator(s)