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Microsoft Copilot & Azure AIAI-300T00: Operationalize machine learning and generative AI solutions
Operationalise machine learning and generative AI on Azure.
4 daysIntermediate13 modulesAI-300T00
About this course
Operationalise machine learning and generative AI on Azure. Over four days you will run experiments and tune hyperparameters in Azure Machine Learning, build and trigger pipelines, deploy and monitor models and manage generative AI in production.
Curriculum
13 modules
- 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
Who it's for
ML engineers and MLOps practitioners who run models in production.
Before you start
No formal prerequisites. Bring curiosity and a willingness to practise.
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