MLOps Fundamentals – Learn MLOps Concepts with Azure demo Course Syllabus

Overview: This course provides a comprehensive introduction to MLOps, guiding learners through the full lifecycle of machine learning operations with hands-on implementation using Azure and industry-standard tools. Designed for beginners with some foundational knowledge, the course spans approximately 10 hours of content, combining theory, practical demonstrations, and real-world project implementation. You’ll gain proficiency in version control, experiment tracking, containerization, CI/CD automation, orchestration, and model monitoring—equipping you to deploy and manage ML models effectively in production environments.

Module 1: Introduction to MLOps

Estimated time: 0.5 hours

• What is MLOps and its role in modern ML systems
• Key challenges in deploying and managing ML models
• Importance of reproducibility and collaboration
• Overview of the MLOps lifecycle

Module 2: ML Lifecycle & Pipeline Structure

Estimated time: 0.75 hours

• Stages of the machine learning lifecycle: development, training, validation, deployment, and monitoring
• Designing scalable and repeatable ML pipelines
• Integration of automated workflows
• Best practices for pipeline modularity and maintainability

Module 3: Version Control with Git & DVC

Estimated time: 1 hour

• Using Git for code versioning in ML projects
• Tracking dataset versions with DVC (Data Version Control)
• Ensuring reproducibility across environments
• Collaborative workflows for ML teams

Module 4: MLflow for Experiment Tracking

Estimated time: 1 hour

• Logging experiments, parameters, and metrics using MLflow
• Organizing runs and comparing model performance
• Using MLflow Model Registry for version management
• Setting up a tracking server for team collaboration

Module 5: Containerization with Docker

Estimated time: 0.75 hours

• Introduction to Docker and containerization concepts
• Creating Docker images for ML environments
• Ensuring consistency across development and production

Module 6: CI/CD Pipelines for ML Projects

Estimated time: 1 hour

• Automating training and testing workflows
• Implementing CI/CD using GitHub Actions and Jenkins
• Validating models before deployment
• Enabling continuous integration for ML code and data

Module 7: Orchestration with Airflow/Kubeflow

Estimated time: 1 hour

• Managing complex ML workflows using Airflow and Kubeflow
• Scheduling model training and deployment pipelines
• Handling dependencies and failure recovery
• Monitoring workflow execution and logs

Module 8: Model Serving & Monitoring

Estimated time: 1 hour

• Deploying models using batch and real-time serving
• Implementing A/B testing and canary deployments
• Monitoring model performance and data drift
• Establishing feedback loops for model retraining

Module 9: Real-World Project: End-to-End MLOps

Estimated time: 1.25 hours

• Building a complete MLOps pipeline from data to deployment
• Integrating Git, DVC, MLflow, Docker, and CI/CD
• Deploying a model on Azure with monitoring in place
• Reviewing best practices and lessons learned

Prerequisites

• Basic understanding of machine learning concepts
• Familiarity with Python and command-line tools
• Some experience with cloud platforms (helpful but not required)

What You'll Be Able to Do After

• Explain the core principles and stages of the MLOps lifecycle
• Implement version control for code and datasets using Git and DVC
• Track and manage ML experiments and models using MLflow
• Containerize ML applications using Docker for consistent deployment
• Design and automate CI/CD pipelines for machine learning workflows