This course prepares participants to design, implement, and operate Machine Learning Operations (MLOps) and Generative AI Operations (GenAIOps) solutions on Azure. Participants will learn abobut 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. Participants gain experience with automation, continuous integration and delivery, infrastructure as code, and observability using tools such as GitHub Actions, Azure CLI, and Bicep. The course emphasizes delivering reliable, production-ready AI systems aligned with modern MLOps and GenAIOps best practices.
Prerequisites:
- Experience with Python
- A foundational understanding of machine learning concepts
- Basic familiarity with DevOps practices, such as source control, CI/CD, and command-line tools
Purpose
| Design, implement, and operate Machine Learning Operations (MLOps) and Generative AI Operations (GenAIOps) solutions on Azure |
Audience
| IT professionals who want to design and operate production-grade AI solutions on Azure and are preparing to implement MLOps and GenAIOps workflows using Azure-native services |
Role
| Data Scientists |Â Machine Learning Engineers |Â DevOps Professionals |
SkillLevel
| Intermediate |
Style
| Lecture | Hands-on Activities | Labs |
Duration
| 4 days |
Related Technologies
| Azure | Python | GitHub | Microsoft Foundry |
Learning objectives
- Run pipelines in Azure Machine Learning
- Perform hyperparameter tuning for machine learning models
- Manage Azure Machine Learning jobs using GitHub Actions
- Plan and prepare a GenAIOps solution
- Manage prompts for AI agents in Microsoft Foundry using GitHub
- Evaluate and optimize AI agents through structured experiments
- Utilize Microsoft Foundry and GitHub Actions to automate AI evaluations
- Analyze and debug generative AI applications using tracing
