course
Operationalize machine learning and generative AI solutions (AI-300)
Learn how to build and manage Azure-based MLOps and GenAIOps solutions from development through deployment.
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.
Audience Profile
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 who are preparing to implement MLOps and GenAIOps workflows using Azure-native services.
Certification
This course prepares you for the Associate exam AI-300 (Microsoft Certified: Machine Learning Operations Engineer Associate).
Learning Goals
 Implement end-to-end machine learning operations (MLOps) with Azure Machine Learning | Apply  |
Prior Knowledge
Experience in Python Foundational understanding of machine learning concepts Basic familiarity with DevOps practices such as source control, CI/CD, and command-line tools
Subjects
- Operationalize machine learning models (MLOps)
- Operationalize generative AI applications (GenAIOps)
1. Operationalize machine learning models (MLOps)
- 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
2. Operationalize generative AI applications (GenAIOps)
- Plan and prepare GenAIOps solutions
- 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
Schedule
All courses can also be conducted within your organization as customized or incompany training.
Our training advisors are happy to help you provide personal advice or find Incompany training within your organization.
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Certification
The knowledge from this training aligns with these certifications.
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