Operationalize machine learning and generative AI solutions

Operationalize machine learning and generative AI solutions (AI-300T00)

Delivery methods

On-Site, Virtual

Duration

4 days

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

Implement end-to-end MLOps workflows using Azure Machine Learning.
Automate model training, experimentation, and hyperparameter tuning.
Build and manage machine learning pipelines for scalable workflows.
Integrate GitHub Actions to enable CI/CD automation for ML models.
Deploy, monitor, and manage machine learning and generative AI applications in production.
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

1Operationalize 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

2Operationalize 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

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