AI-300T00: Operationalize machine learning and generative AI solutions
5 Based on 213 reviews- 331,952 Learners
- 287 Courses
Upcoming Class Schedule
$2,495.00
Jan 18 - Jan 21, 2027
9:00 AM - 5:00 PM Eastern- VILT
- Jan 18 - Jan 21, 2027
- 9:00 AM - 5:00 PM Eastern
- VILT
Who should attend AI-300T00: Operationalize machine learning and generative AI solutions Course
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.
AI-300T00: Operationalize machine learning and generative AI solutions Course Outline
- 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
- Define a search space
- Configure a sampling method
- Configure early termination
- Use a sweep job for hyperparameter tuning
- Module assessment
- Create components
- Create a pipeline
- Run a pipeline job
- Module assessment
- Understand the business problem
- Explore the solution architecture
- Use GitHub Actions for model training
- Module assessment
- Understand the business problem
- Explore the solution architecture
- Trigger a workflow
- Module assessment
- Understand the business problem
- Explore the solution architecture
- Set up environments
- Module assessment
- Understand the business problem
- Explore the solution architecture
- Model deployment
- Module assessment
- 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
- Apply version control to prompts
- Understand Microsoft Foundry agents and prompt versioning
- Organize prompts in GitHub repositories
- Develop safe prompt deployment workflows
- Design evaluation experiments
- Apply Git-based workflows to optimization experiments
- Apply evaluation rubrics for consistent scoring
- Understand why automated evaluations matter
- Align evaluators with human criteria
- Create evaluation datasets
- Implement batch evaluations with Python
- Integrate evaluations into GitHub Actions
- 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
- 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
Resources
FAQs on AI-300T00: Operationalize machine learning and generative AI solutions
This course is designed for data scientists, machine learning engineers, and DevOps professionals who want to design and operate production-grade AI solutions on Azure, specifically those preparing to implement MLOps and GenAIOps workflows using Azure-native services.
Learners should have experience with Python, a foundational understanding of machine learning concepts, and basic familiarity with DevOps practices such as source control, CI/CD, and command-line tools.
The course covers experimenting with Azure Machine Learning, hyperparameter tuning, running pipelines, triggering ML jobs with GitHub Actions, feature-based development workflows, and operationalizing both machine learning and generative AI solutions on Azure.
You will learn to preprocess data, run automated ML experiments, track models with MLflow, tune hyperparameters, build and run ML pipelines, and integrate GitHub Actions to automate model training and deployment workflows (MLOps and GenAIOps).
Yes, each module includes hands-on exercises and a module assessment to reinforce learning and validate understanding of key concepts like pipeline creation, hyperparameter tuning, and CI/CD integration with Azure Machine Learning.