Training on Azure MLOps

Duration: Hours

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    Training Mode: Online

    Description

    TABLE OF CONTENT

    Introduction to Azure MLOps

    1.1 Overview of Azure MLOps
    1.2 Importance of MLOps in Machine Learning
    1.3 Key Concepts and Terminology

    Setting Up Azure Machine Learning Environment

    2.1 Creating an Azure Machine Learning Workspace
    2.2 Configuring Development Environments
    2.3 Integrating Azure DevOps with Azure Machine Learning

    Version Control for Machine Learning Projects

    3.1 Introduction to Git and Azure Repos
    3.2 Branching Strategies for ML Projects
    3.3 Collaborative Development with Azure Repos

    Continuous Integration (CI) for ML Models

    4.1 Automating Model Build Pipelines
    4.2 Leveraging Azure Pipelines for CI
    4.3 Running Tests and Quality Checks in CI

    Continuous Deployment (CD) for ML Models

    5.1 Deploying Models to Azure Kubernetes Service (AKS)
    5.2 Azure Container Instances (ACI) Deployment
    5.3 Configuring Model Deployment Pipelines

    Monitoring and Logging in MLOps

    6.1 Implementing Model Monitoring with Azure Monitor
    6.2 Logging Best Practices for ML Models
    6.3 Alerting and Notifications for Model Performance Issues

    Scaling and Optimization

    7.1 Scaling ML Workloads with Azure Machine Learning
    7.2 Performance Optimization Strategies
    7.3 Cost Management in MLOps

    Governance and Compliance in MLOps

    8.1 Role-Based Access Control (RBAC) in Azure
    8.2 Data Privacy and Compliance Considerations
    8.3 Auditing and Reporting for MLOps

    Collaboration and Communication

    9.1 Integrating Azure Boards for ML Project Management
    9.2 Using Teams and Communication Channels
    9.3 Collaborative Tools for Data Science Teams

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