Live Microsoft AI-300 Training Course

Guaranteed to Run

Upcoming Dates	Class Times	Class Format	Quote
1/18 - 1/21, 2027	9:00 AM - 5:00 PM ET 8:00 AM - 4:00 PM CT 6:00 AM - 2:00 PM PT 3:00 AM - 11:00 AM HT	Instructor-Led	Instant Quote

Instructor-Led

Certified Instructor

Includes all course materials

Instant Quote

Can't travel or you want to stay with your family or business. No problem!

Stay in your own city and save the additional expenses of roundtrip airfare, lodging, transportation, and meals and receive the same great instruction live from our instructors in our Live Instructor-Led Remote Classroom Training.

Remote Classroom Training

Our Remote Classroom Training is a live class with students observing the instructor and listening through your computer speakers.

You will see the instructor's computer, slides, notes, etc., just like in the classroom. You will be following along, doing work, labs, and individual assignments.

CED Solutions Rewards Points Program

"Made the subject matter fun and was inclusive of all levels of participants."

-Pat Brannen, Atlanta, GA

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.

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.

Course Content

1 - Experiment with Azure Machine Learning

• Preprocess data and configure featurization
• Run an automated machine learning experiment
• Evaluate and compare models
• Configure MLflow for model tracking in notebooks
• Train and track models in notebooks
• Evaluate models with the Responsible AI dashboard
• Module assessment

2 - Perform hyperparameter tuning with Azure Machine Learning

• Define a search space
• Configure a sampling method
• Configure early termination
• Use a sweep job for hyperparameter tuning
• Module assessment

3 - Run pipelines in Azure Machine Learning

• Create components
• Create a pipeline
• Run a pipeline job
• Module assessment

4 - Trigger Azure Machine Learning jobs with GitHub Actions

• Understand the business problem
• Explore the solution architecture
• Use GitHub Actions for model training
• Module assessment

5 - Trigger GitHub Actions with feature-based development

• Understand the business problem
• Explore the solution architecture
• Trigger a workflow
• Module assessment

6 - Work with environments in GitHub Actions

• Understand the business problem
• Explore the solution architecture
• Set up environments
• Module assessment

7 - Deploy a model with GitHub Actions

• Understand the business problem
• Explore the solution architecture
• Model deployment
• Module assessment

8 - Plan and prepare a GenAIOps solution

• Explore use cases for GenAIOps
• Select the right generative AI model
• Understand the development lifecycle of a language model application
• Explore available tools and frameworks to implement GenAIOps
• Module assessment

9 - Manage prompts for agents in Microsoft Foundry with GitHub

• Apply version control to prompts
• Understand Microsoft Foundry agents and prompt versioning
• Organize prompts in GitHub repositories
• Develop safe prompt deployment workflows

10 - Evaluate and optimize AI agents through structured experiments

• Design evaluation experiments
• Apply Git-based workflows to optimization experiments
• Apply evaluation rubrics for consistent scoring

11 - Automate AI evaluations with Microsoft Foundry and GitHub Actions

• Understand why automated evaluations matter
• Align evaluators with human criteria
• Create evaluation datasets
• Implement batch evaluations with Python
• Integrate evaluations into GitHub Actions

12 - Monitor your generative AI application

• Why do you need to monitor?
• Understand key metrics to monitor
• Explore how to monitor with Azure
• Integrate monitoring into your app
• Interpret monitoring results

13 - Analyze and debug your generative AI app with tracing

• Why do you need to use tracing?
• Identify what to trace in generative AI applications
• Implement tracing in generative AI applications
• Debug complex workflows with advanced tracing patterns
• Make informed decisions with trace data analysis

CED Solutions is your best choice for AI-300, AI-300 training, AI-300 certification, AI-300 boot camp, AI-300 certification training, AI-300 certification course, AI-300 course, AI-300 class.