Azure Machine Learning Comprehensive Course (End-to-End MLOps on Azure)

This comprehensive Azure Machine Learning course is designed for data scientists, machine learning engineers, and developers who want to build, train, and deploy machine learning models using Microsoft Azure. The course provides a complete journey from foundational ML concepts to advanced MLOps workflows used in production environments.

Learners gain hands-on experience with both no-code and code-first approaches, enabling them to work across different levels of Azure Machine Learning.

1.1 Introduction to Azure Machine Learning

This section introduces the core principles of Azure Machine Learning and explains how it fits into modern data science and AI pipelines.

Learners understand how Azure ML supports the full machine learning lifecycle from data preparation to deployment.

1.1.1 Core Concepts of Azure ML

This part explains the foundational ideas behind Azure Machine Learning and its role in building scalable AI systems.

1.1.2 Azure ML in Modern Data Science Workflows

Here learners explore how Azure ML integrates into enterprise-level data science pipelines.

1.2 Automated Machine Learning (AutoML) and Azure ML Designer

This section focuses on no-code and low-code tools that simplify machine learning model development.

1.2.1 Building Models with AutoML

This topic explains how Azure automatically trains and selects the best machine learning models.

1.2.2 Visual Model Building with Azure ML Designer

Here learners explore drag-and-drop workflows for building machine learning pipelines visually.

1.3 Code-First Machine Learning with Azure ML SDK

This section introduces programmatic machine learning development using Python and Azure ML SDK.

1.3.1 Training Models with Python

This topic explains how to build and train machine learning models using code.

1.3.2 Azure ML SDK Workflow Development

Here learners explore how to manage full ML pipelines using Python-based workflows.

1.4 Model Deployment and Operationalization

This section focuses on deploying machine learning models and making them available for real-world use.

1.4.1 Deploying Machine Learning Models in Azure

This topic explains how to publish trained models as production services.

1.4.2 Real-World Model Consumption

Here learners explore how deployed models are used in applications and business systems.

1.5 MLOps and Model Lifecycle Management

This section introduces MLOps practices for managing the full lifecycle of machine learning models in Azure.

1.5.1 MLflow Integration and Experiment Tracking

This topic explains how MLflow is used to track experiments, metrics, and model performance.

1.5.2 Model Versioning and Lifecycle Control

Here learners explore how to manage different versions of machine learning models efficiently.

1.6 CI/CD with Azure ML and GitHub Actions

This section focuses on automating machine learning workflows using DevOps tools.

1.6.1 Automating ML Pipelines with GitHub Actions

This topic explains how to automate training and deployment workflows.

1.6.2 Collaboration Between ML and DevOps Teams

Here learners understand how CI/CD improves teamwork and production reliability.

1.7 Final Skills and Learning Outcomes

By the end of this course, learners will be able to design, build, train, deploy, and manage machine learning systems using Azure Machine Learning.

They will also understand how to implement full MLOps workflows using MLflow, Azure SDK, and GitHub Actions for scalable production AI systems.