Cloud and AI Platform
Innovate with Azure AI Apps and Agents
Intermediate
Azure Machine LearningAzure Machine Learning and MLOps
This course covers the essentials of machine learning and how to leverage Azure 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.
16 hours(Suggested: 4 days, 4h/day)TechnicalProject ReadyHands-on Labs
MS Course ID: 00017
Last Updated: Jul 1, 2025
Course Syllabus
1
Introduction to Azure Machine Learning
• What is machine learning?
• What is Azure Machine Learning?
• Azure Machine Learning CLI and Python SDK v2
• Creating ML resources and getting started with Azure Machine Learning
• Working with Data
• Overview of Data concepts in Azure Machine Learning
• Creating datastores
• Creating connections (preview)
• Understanding Managed feature store
180 mins
Lecture
2
Hands on labs
• Prepare dataset, train and deploy a classification model, using Azure Machine Learning Studio
• Create a labeled dataset using Azure Machine Learning data labeling tools
• Develop and register a feature set with managed feature store and train models by using features
60 mins
Lab
3
Automating and deploying Azure Machine Learning models
• Training models with Azure Machine Learning
• Overview of Automated machine learning (AutoML)
• Deploying Azure ML models
• Monitoring models with Azure Machine Learning
• Prompt flow and LLM Ops
• Semantic Kernel
• MLflow and Azure Machine Learning
180 mins
Lecture
4
Hands on labs
• Train a classification model with no-code Auto ML in the Azure Machine Learning studio
• Forecast demand with no-code Automated Machine Learning in the Azure Machine Learning studio
• Train the best Regression model for the Hardware dataset
60 mins
Lab
5
Using Generative AI in Azure Machine Learning
• Working with Azure Machine Learning pipelines andcomponents
• Understanding Model Catalogand Collections
• Overview of Azure MachineLearning prompt flow
• Understanding Retrieval Augmented Generation using Azure Machine Learning promptflow (preview)
• Implementing Vector stores in Azure Machine Learning(preview)
• Model monitoring for generative AI applications (preview)
180 mins
Lecture
6
Hands on labs
• Develop and test prompt flow from Azure Machine Learning Studio
• Implementing QA data generation with RAG using a prompt flow
60 mins
Lab
7
Operationalize with MLOps
• Operationalize with MLOps
• Introduction to Git integrationfor Azure Machine Learning
• Using Azure Pipelines with Azure Machine Learning
• Using GitHub Actions with Azure Machine Learning
• GenAIOps (LLMOps) for MLOps practitioners
• Implementing GenAIOps with prompt flow and GitHub
• Securing AI Applications onAzure
• Implementing Security and governance for Azure Machine Learning
• Responsible use of AI
• Configuring Responsible AI dashboards
• Sharing Responsible AI insights using the Responsible AI scorecard (preview)
180 mins
Lecture
8
Hands on labs
• Set up MLOps with GitHub
• Using the Responsible AI dashboard to improve performance of machine learning models and perform Model Analysis
60 mins
Lab
What You'll Learn
Understand core concepts and best practices
Hands-on experience with real-world scenarios
Learn from certified Microsoft experts
Prepare for relevant certifications
Access to lab environments
Post-training support and resources
Course Details
- Duration
- 16 hours
- Level
- Intermediate
- Role
- Technical
- Course Type
- Project Ready
- Partner Segment
- SMB, Enterprise
- Course Stage
- Available
- Hands-on Labs
- Yes