Microsoft LevelUp - Azure Machine Learning and MLOps

Azure Machine Learning and MLOps

Duration - 16 Hours

Level - Intermediate

No. of learners - 19

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Last Updated Apr 2025

Style

Self paced

Target Audience

Technical Project Ready with Labs

Certification

Hands on Labs

Yes

Solution Areas

Azure - Data and AI, Innovate with Azure AI Platform

Course Overview

Overview

This course covers the essentials of machine learning and how to leverage Azure’s platform for building, deploying, and managing ML models. You’ll learn about creating and managing the end-to-end lifecycle of ML models using Azure Machine Learning Studio and Azure AI Studio. You will understand the implementation of Generative AI. You’ll explore advanced MLOps features for automating the ML lifecycle, GenAIOps and monitoring model performance.

Course Modules

Modules

Introduction to Azure Machine Learning

Learn the fundamentals of machine learning and Azure Machine Learning, including tools like the CLI, Python SDK v2, and AI Studio. Explore creating ML resources, managing data concepts, and working with datastores and connections. Gain insights into data preparation with Apache Spark and leverage the Managed Feature Store for enhanced ML workflows.

Automating and deploying Azure Machine Learning models

Master the end-to-end process of training, deploying, and monitoring machine learning models with Azure Machine Learning. Explore Automated Machine Learning (AutoML), MLflow integration, and the use of ML pipelines and components to streamline workflows and optimize model performance. Learn to deploy and monitor models efficiently, ensuring robust and scalable ML solutions.

Using Generative AI in Azure Machine Learning

Dive into advanced Azure Machine Learning features, including Model Catalog, Collections, and prompt flow for streamlined workflows. Explore Retrieval Augmented Generation and Vector Stores (preview) to enhance generative AI applications. Learn effective strategies for monitoring and optimizing models for robust AI solutions.

Operationalize with MLOps

Master MLOps with Azure Machine Learning, including Git integration, Azure Pipelines, and GitHub Actions. Explore GenAIOps for LLM workflows, secure AI applications, and implement robust security and governance practices. Learn Responsible AI techniques, configure dashboards, and share insights with the Responsible AI scorecard (preview) for ethical and effective AI solutions.