MLOps | Machine Learning Operations Specialization course Syllabus

This specialization provides a hands-on introduction to MLOps, guiding learners through the end-to-end lifecycle of operationalizing machine learning models in production. Structured across five core modules and a final capstone project, the course blends theory with practical implementation. Learners will spend approximately 3–4 weeks per module, with a total time commitment of around 120 hours. The curriculum emphasizes real-world workflows, cloud deployment, automation, and monitoring, culminating in a comprehensive project that demonstrates production-level MLOps proficiency.

Module 1: Foundations of MLOps

Estimated time: 30 hours

• Understand the MLOps lifecycle and its importance in AI engineering
• Explore integration of DevOps principles into machine learning workflows
• Learn version control practices for models and data
• Study reproducibility, automation, and metadata tracking fundamentals

Module 2: Continuous Integration & Deployment (CI/CD)

Estimated time: 30 hours

• Build automated machine learning pipelines
• Implement testing strategies for models and data pipelines
• Deploy ML models using cloud infrastructure
• Manage containerization with Docker and orchestration tools

Module 3: Model Monitoring & Maintenance

Estimated time: 30 hours

• Track model performance in production environments
• Detect data drift, concept drift, and model decay
• Implement logging, alerting, and monitoring systems
• Design automated retraining workflows

Module 4: Scalable ML Systems

Estimated time: 30 hours

• Design scalable and reliable ML infrastructure
• Apply infrastructure-as-code practices using tools like Terraform
• Optimize deployment architectures for performance and cost
• Integrate security and access controls in ML systems

Module 5: Capstone Project

Estimated time: 30 hours

• Design an end-to-end ML production system
• Implement CI/CD, monitoring, and retraining pipelines
• Deploy a model on cloud infrastructure with full automation

Module 6: Final Project

Estimated time: 30 hours

• Deliverable 1: A fully documented MLOps pipeline
• Deliverable 2: Cloud-deployed ML model with monitoring in place
• Deliverable 3: Automated retraining and performance reporting system

Prerequisites

• Working knowledge of Python programming
• Familiarity with machine learning concepts and workflows
• Basic understanding of cloud platforms (e.g., AWS, GCP, or Azure)

What You'll Be Able to Do After

• Operationalize machine learning models in production environments
• Design and implement CI/CD pipelines for ML systems
• Monitor, maintain, and retrain models in real-world settings
• Deploy scalable ML infrastructure using cloud platforms
• Earn a university-backed credential to advance in AI engineering roles