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Classes AI/ML

AI-300T00 Microsoft Technical Partner Delivered

AI-300T00: Operationalize machine learning and generative AI solutions

  • 4 days
  • 2 upcoming dates
  • 2 guaranteed to run
  • May qualify for CEU/PDU credit

Upcoming dates

All times shown in the class's own timezone. 2 of 2 are guaranteed to run, meaning they go ahead regardless of enrollment.

Dates Starts Where Status Seat Book
Jan 18-21 9:00 AM to 5:00 PM EST Live virtual GUARANTEED $2,495
Jan 18-21 9:00 AM to 5:00 PM EST Live virtual GUARANTEED $2,495

About this class

We have 2 scheduled dates of AI-300T00: Operationalize machine learning and generative AI solutions, starting Jan 18-21. Every one is guaranteed to run regardless of enrollment. Seats are $2,495 each over 4 days, courseware included. Book online with a card, or send a purchase order and we will invoice on net 30 terms.

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.

Course outline

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

Who this 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.

How reserving works. Your card is authorized, not charged. We confirm the seat with the training center, usually within one business day, and take payment only once it is held. If the class turns out to be full we release the authorization and you are not charged.

Request a quote

Tell us how many people and roughly when. We'll come back with dates, seat price, and a total by the end of the next business day.

Class AI-300T00: Operationalize machine learning and generative AI solutions

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