Azure Machine Learning & MLOps : Beginner to Advance Course Syllabus

Overview (80-120 words) describing structure and time commitment.

Module 1: Introduction to MLOps on Azure

Estimated time: 0.5 hours

• Understanding the MLOps lifecycle and its role in scalable machine learning
• Key components of MLOps: experimentation, deployment, monitoring
• How Azure Machine Learning enables end-to-end MLOps workflows
• Use cases and industry applications of cloud-native MLOps

Module 2: Azure Machine Learning Workspace Setup

Estimated time: 0.75 hours

• Creating and configuring an Azure Machine Learning workspace
• Setting up compute resources and environments
• Managing access and permissions for team collaboration
• Integrating Azure ML with cloud storage and key services

Module 3: Model Training and Experimentation

Estimated time: 1 hour

• Running machine learning experiments using Azure ML SDK
• Logging metrics, parameters, and outputs with MLflow integration
• Managing compute targets for training workloads
• Performing hyperparameter tuning with Azure ML Hyperdrive

Module 4: ML Pipelines & Automation

Estimated time: 1 hour

• Designing reusable ML pipelines with modular steps
• Defining data dependencies and pipeline scheduling
• Automating workflows using Azure ML Pipelines
• Integrating CI/CD with GitHub Actions for MLOps automation

Module 5: Model Registration and Deployment

Estimated time: 1 hour

• Registering trained models in Azure ML model registry
• Deploying models as web services using ACI and AKS
• Configuring endpoints, authentication, and scaling options
• Managing model versions and rollback strategies

Module 6: Monitoring & Lifecycle Management

Estimated time: 0.75 hours

• Monitoring model performance and prediction drift
• Detecting data drift and triggering retraining workflows
• Setting up alerts and feedback loops for continuous improvement
• Implementing governance and model lifecycle best practices

Module 7: End-to-End Project Walkthrough

Estimated time: 1.25 hours

• Building a complete ML project from data to deployment
• Applying MLOps practices: version control, pipelines, monitoring
• Addressing real-world challenges and debugging deployment issues
• Final deliverable: Fully operationalized model with documentation

Prerequisites

• Familiarity with Azure fundamentals and cloud services
• Basic knowledge of Python and machine learning concepts
• Experience with Git version control recommended

What You'll Be Able to Do After

• Understand and implement the full MLOps lifecycle on Azure
• Build, train, and log ML models using Azure ML and MLflow
• Design and automate scalable ML pipelines with CI/CD integration
• Deploy models securely as managed endpoints with monitoring
• Apply best practices for model governance, retraining, and observability