Azure Machine Learning & MLOps : Beginner to Advance

Module 1: Introduction to MLOps on Azure

⏳ 30 minutes

• Overview of MLOps and its importance in scalable ML solutions.

• Role of Azure Machine Learning in operationalizing AI.

Module 2: Azure Machine Learning Workspace Setup

⏳ 45 minutes

• Creating and configuring Azure ML workspaces and environments.

• Resource setup and permissions for teams.

Module 3: Model Training and Experimentation

⏳ 60 minutes

• Running and logging experiments using MLflow and Azure ML SDK.

• Hyperparameter tuning and compute management.

Module 4: ML Pipelines & Automation

⏳ 60 minutes

• Building reusable training pipelines with steps and data dependencies.

• Automating with Azure ML Pipelines and GitHub Actions.

Module 5: Model Registration and Deployment

⏳ 60 minutes

• Registering models and deploying as web services (ACI/AKS).

• Endpoint management and authentication.

Module 6: Monitoring & Lifecycle Management

⏳ 45 minutes

• Tracking model performance and detecting data drift.

• Implementing feedback loops and retraining triggers.

Module 7: End-to-End Project Walkthrough

⏳ 75 minutes

• Building, deploying, and monitoring a complete ML project.

• Best practices, challenges, and governance tips.

Get certificate