AI-300: Operationalize machine learning and generative AI solutions

• Design and implement an MLOps infrastructure
• Implement machine learning model lifecycle and operations
• Design and implement a GenAIOps infrastructure
• Implement generative AI quality assurance and observability
• Optimize generative AI systems and model performance

• Programming experience with Python or R
• Experience developing and training machine learning models
• Familiarity with basic Azure Machine Learning concepts

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.

• 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
• 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

Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate
 
Demonstrate skills setting up infrastructure for machine learning operations (MLOps) and generative AI operations (GenAIOps) solutions on Azure, together referred to as AI operations (AIOps).
 
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). You need experience training, optimizing, deploying, and maintaining traditional machine learning models by using Azure Machine Learning, in addition to experience deploying, evaluating, monitoring, and optimizing generative AI applications and agents by using Microsoft Foundry.
 
You should have a data science background with experience in Python programming and an entry-level understanding of DevOps practices, including using tools like GitHub Actions and working with command-line interfaces (CLIs).