Operationalize machine learning and generative AI solutions
(4 days)
Course Description
This course prepares learners to design, implement, and operate Machine Learning Operations (MLOps) and Generative AI Operations (GenAIOps) solutions on Azure. It covers building secure and scalable AI infrastructure, managing the full lifecycle of traditional machine learning models with Azure Machine Learning, and deploying, evaluating, monitoring, and optimizing generative AI applications and agents using Microsoft Foundry. Learners will gain hands-on knowledge of automation, continuous integration and delivery, infrastructure as code, and observability by using tools such as GitHub Actions, Azure CLI, and Bicep. The course emphasizes collaboration with data science and DevOps teams to deliver reliable, production-ready AI systems aligned with modern MLOps and GenAIOps best practices.Learning Objectives
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Implement end-to-end MLOps workflows using Azure Machine Learning.
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Automate model training, experimentation, and hyperparameter tuning.
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Build and manage machine learning pipelines for scalable workflows.
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Integrate GitHub Actions to enable CI/CD automation for ML models.
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Deploy, monitor, and manage machine learning and generative AI applications in production.
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Evaluate, optimize, and debug AI systems using GenAIOps practices, monitoring, and tracing tools.
Who Should Attend
This course is intended for data scientists, machine learning engineers, and DevOps professionals who want to design and operate production-grade AI solutions on Azure. It is suited for learners with experience in Python, a foundational understanding of machine learning concepts, and basic familiarity with DevOps practices such as source control, CI/CD, and command-line tools, who are preparing to implement MLOps and GenAIOps workflows using Azure-native services.
Prerequisites
- Programming experience with Python or R
- Experience developing and training machine learning models
- Familiarity with basic Azure Machine Learning concepts
Course Outline
Module 1: Operationalize machine learning models MLOps
Experiment with Azure Machine Learning
Exercise – Find the best classification model with Azure Machine Learning
Perform hyperparameter tuning with Azure Machine Learning
Exercise – Run a sweep job
Run pipelines in Azure Machine Learning
Exercise – Run a pipeline job
Trigger Azure Machine Learning jobs with GitHub Actions
Exercise
Trigger GitHub Actions with feature-based development
Exercise
Trigger GitHub Actions with feature-based development
Exercise
Work with environments in GitHub Actions
Exercise
Deploy a model with GitHub Actions
Exercise
Module 2: Operationalize generative AI applications GenAIOps
Plan and prepare a GenAIOps solution
Exercise – Compare language models from the model catalog
Manage prompts for agents in Microsoft Foundry with GitHub
Exercise – Develop prompt and agent versions
Evaluate and optimize AI agents through structured experiments
Exercise – Evaluate and compare AI agent versions
Automate AI evaluations with Microsoft Foundry and GitHub Actions
Exercise – Set up automated evaluations
Monitor your generative AI application
Exercise – Enable monitoring for a generative AI application
Analyze and debug your generative AI app with tracing
Exercise – Enable tracing for a generative AI application
