The complete Azure Machine learning course – 2025 Edition

This module lays the foundation for your journey into Machine Learning (ML) and Microsoft Azure’s AI ecosystem. You’ll explore the core principles of ML — from understanding data-driven decision-making to differentiating between supervised, unsupervised, and reinforcement learning. The module also introduces Azure Machine Learning Studio, its features, and how it simplifies model development, training, and deployment in the cloud.

This module focuses on the foundation of any successful machine learning project — data. You’ll learn how to import, explore, and clean datasets in Azure Machine Learning Studio, handle missing values and outliers, and prepare data for modeling. The module also covers feature engineering, data splitting, and normalization techniques to ensure your models are accurate, balanced, and ready for training.

In this module, you’ll learn how to build, train, and customize machine learning models using Azure Machine Learning Studio. You’ll explore how to choose the right algorithm for regression, classification, and clustering problems, apply feature engineering techniques, and fine-tune model parameters. The module also includes hands-on demos for building and optimizing both basic and advanced models — from decision trees to XGBoost and neural networks.

This module focuses on evaluating, fine-tuning, and optimizing machine learning models to achieve the best performance. You’ll learn how to use key evaluation metrics like accuracy, precision, recall, and cross-validation to compare models. It also covers advanced optimization techniques such as hyperparameter tuning, regularization, and ensembling to enhance model accuracy and efficiency. Finally, you’ll explore model monitoring and updating practices to maintain performance and manage the full model lifecycle in Azure ML.

This module introduces the concept of machine learning pipelines and their role in automating and streamlining ML workflows. You’ll learn how to design reusable, scalable pipelines in Azure ML Studio — from data ingestion to model deployment. The module also covers integrating custom Python scripts, managing dependencies, and automating pipeline runs with triggers. Advanced topics include dynamic pipeline parameters, error handling, and complex multi-step workflows, enabling you to build robust and production-ready ML systems.

This module dives into the advanced techniques of training and deploying machine learning models using Azure’s scalable infrastructure. You’ll explore distributed training with frameworks like TensorFlow and PyTorch, leveraging Azure Compute Clusters for high-performance workloads. The module also covers real-time and batch inference, secure endpoint management, and monitoring deployed models using Azure Monitor. Finally, you’ll learn advanced deployment strategies, including edge deployments with Azure IoT and model optimization using ONNX, preparing you to operationalize AI models efficiently and securely.

This module introduces MLOps, the practice of applying DevOps principles to machine learning workflows. You’ll learn how to operationalize ML models for continuous integration, delivery, and monitoring using Azure ML and DevOps tools. The module covers automating ML pipelines, version control, infrastructure as code (IaC), and implementing security and compliance best practices. By the end, you’ll understand how to maintain reliable, scalable, and secure ML systems in production.

This module delves into Generative AI and its practical applications using Azure ML Studio. You’ll explore popular models like GPT, DALL·E, Stable Diffusion, and Codex, learning how to generate text, images, and code. The module also covers fine-tuning models for domain-specific tasks, creating custom chatbots, and implementing ethical AI practices such as bias detection, fairness analysis, and explainability. Hands-on labs help you build, deploy, and audit generative AI solutions effectively in real-world scenarios.