Program Outline
Operationalize machine learning and generative AI solutions
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.
Delivery
Virtual, On-site, or Hybrid
Duration
4 days
Product
Azure
Role
AI Engineer
Microsoft
Machine Learning, Artificial IntelligenceMachine Learning Operations (MLOps) Engineer Associate (beta)
Azure Machine Learning, Microsoft Foundry
Best Fit
Audience Profile
Who This Program Is For
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.
Overview
Program Summary
As a candidate for this Microsoft Certification, you should have subject matter expertise in setting up infrastructure for machine learning operations (MLOps) and generative AI operations (GenAIOps) solutions on Azure, together referred to as AI operations (AIOps).
Course Outline
Complete Module Sequence
Review the full module sequence for this program, including the primary topic coverage in each module where available.
1Module 1
Operationalize machine learning models (MLOps)
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Module 1
Operationalize machine learning models (MLOps)
Learn the full MLOps lifecycle for machine learning models, from experimentation and pipeline automation to CI/CD, automated testing, and model deployment in production.
- Experiment with Azure Machine Learning
- Perform hyperparameter tuning with Azure Machine Learning
- Run pipelines in Azure Machine Learning
- Trigger Azure Machine Learning jobs with GitHub Actions
- Trigger GitHub Actions with feature-based development
- Work with environments in GitHub Actions
- Deploy a model with GitHub Actions
2Module 2
Operationalize generative AI applications (GenAIOps)
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Module 2
Operationalize generative AI applications (GenAIOps)
Learn the full GenAIOps lifecycle for generative AI applications, from planning and prompt management to evaluation, automated testing, monitoring, and tracing in production.
- Plan and prepare a GenAIOps solution
- Manage prompts for agents in Microsoft Foundry with GitHub
- Evaluate and optimize AI agents through structured experiments
- Automate AI evaluations with Microsoft Foundry and GitHub Actions
- Monitor your generative AI application
- Analyze and debug your generative AI app with tracing
Coverage Areas
Topic Coverage
Coverage Item 1
Design and implement an MLOps infrastructure
Coverage Item 2
Implement machine learning model lifecycle and operations
Coverage Item 3
Design and implement a GenAIOps infrastructure
Coverage Item 4
Implement generative AI quality assurance and observability
Coverage Item 5
Optimize generative AI systems and model performance
Customization
Adapt This Program for Your Team
We can adapt this program around your team structure, platform priorities, delivery goals, and the scenarios your people need to work through in practice.
- •Align labs to your Microsoft tenant and workload scenarios
- •Add readiness checks and exam preparation reviews
- •Extend delivery with role-specific implementation workshops
