Description

Introduction
Azure Machine Learning provides a robust platform for managing the entire machine learning lifecycle, from development to deployment. This course focuses on implementing MLOps using Azure, enabling scalable, efficient, and secure machine learning operations. Learn to automate workflows, deploy models at scale, and leverage Azure services to optimize your ML pipelines for real-world applications.

Prerequisites

1. Basic knowledge of machine learning and model development.
2. Familiarity with Python and ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
3. Understanding of DevOps practices (CI/CD, containers).
4. Experience with Microsoft Azure cloud services.

Table of Contents

1. Introduction to MLOps on Azure
   1.1 What is MLOps, and Why Azure?
   1.2 Overview of Azure Machine Learning Services
   1.3 Key Components of Azure MLOps: Pipelines, Workspaces, and Model Registry
2. Setting Up the Azure ML Environment
   2.1 Creating and Configuring Azure Machine Learning Workspaces
   2.2 Managing Resources: Compute Instances, Clusters, and Storage
   2.3 Integration with GitHub and Azure DevOps
3. Building Scalable Data Pipelines
   3.1 Automating Data Ingestion with Azure Data Factory
   3.2 Data Preprocessing and Feature Engineering with Azure ML Pipelines
   3.3 Monitoring and Handling Data Drift in Azure ML
4. Developing and Training ML Models on Azure
   4.1 Experiment Management and Logging with Azure ML SDK
   4.2 Automating Model Training Using Azure ML Pipelines
   4.3 Hyperparameter Tuning with Azure AutoML
   4.4 Ensuring Reproducibility with Managed Environments
5. Deploying Machine Learning Models
   5.1 Azure Model Registry: Versioning and Governance
   5.2 Deploying Models to Azure Kubernetes Service (AKS)
   5.3 Real-time and Batch Inferencing with Azure Endpoints
   5.4 Model Deployment Strategies: Blue-Green, Canary, and Shadow Deployments
6. CI/CD for Machine Learning with Azure DevOps
   6.1 Setting Up CI/CD Pipelines for ML Workflows
   6.2 Automating Model Testing and Validation(Ref: MLOps with Kubernetes: Orchestrating Machine Learning Workflows )
   6.3 Continuous Deployment to Azure Environments
   6.4 Integrating with GitHub Actions for ML Lifecycle Automation
7. Monitoring and Maintaining ML Models in Production
   7.1 Real-time Model Monitoring with Azure Application Insights
   7.2 Detecting and Mitigating Model Drift and Decay
   7.3 Implementing Feedback Loops for Model Improvement
   7.4 Automating Retraining Pipelines
8. Scaling ML Workloads with Azure
   8.1 Distributed Training on Azure Compute Clusters
   8.2 Leveraging Azure Synapse Analytics for Large-scale Data Processing
   8.3 Cost Optimization and Resource Management
   8.4 Scaling to Multi-region Deployments
9. Azure Security and Compliance for MLOps
   9.1 Role-based Access Control (RBAC) and Identity Management
   9.2 Data Encryption and Secure Communication in ML Pipelines
   9.3 Managing Compliance with Industry Standards (GDPR, HIPAA, etc.)
   9.4 Best Practices for Building Secure Machine Learning Applications
10. Integrating Business Objectives with MLOps on Azure
    10.1 Defining Business KPIs for ML Projects
    10.2 Aligning Azure MLOps Practices with Business Goals
    10.3 Case Study: Implementing MLOps for a Scalable Business Application
11. Hands-on Labs and Capstone Project
    11.1 Building a Scalable ML Pipeline with Azure ML Studio
    11.2 Deploying and Monitoring a Real-time Model on AKS
    11.3 Implementing a CI/CD Workflow Using Azure DevOps
    11.4 Final Capstone: Creating an End-to-end MLOps Workflow on Azure

Conclusion
Azure simplifies MLOps by offering integrated tools for every stage of the machine learning lifecycle. This course equips you to scale ML models with Azure’s robust infrastructure while ensuring efficiency, security, and alignment with business objectives. By the end, you’ll be ready to implement scalable, production-ready ML solutions using Azure.

Reference