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Course Outline
Introduction to AI Builder and Low-Code AI
- AI Builder capabilities and common business scenarios
- Licensing, governance, and tenant-level considerations
- Overview of Power Platform integrations (Power Apps, Power Automate, Dataverse)
OCR and Form Processing: Structured and Unstructured Documents
- Distinctions between structured templates and free-form documents
- Preparing training data: labeling fields, ensuring sample diversity, and following quality guidelines
- Building an AI Builder form processing model and evaluating extraction accuracy
- Post-processing extracted data: validation, normalization, and error handling
- Hands-on lab: OCR extraction from mixed form types and integration into a processing flow
Prediction Models: Classification and Regression
- Problem framing: distinguishing qualitative (classification) from quantitative (regression) tasks
- Feature preparation and handling missing data within Power Platform workflows
- Training, testing, and interpreting model metrics (accuracy, precision, recall, RMSE)
- Considerations for model explainability and fairness in business applications
- Hands-on lab: building a custom prediction model for churn/score or numeric forecasting
Integration with Power Apps and Power Automate
- Embedding AI Builder models into canvas and model-driven apps
- Creating automated flows to process extracted data and trigger business actions
- Design patterns for scalable, maintainable AI-driven applications
- Hands-on lab: end-to-end scenario — document upload, OCR, prediction, and workflow automation
Complementary Process Mining Concepts (Optional)
- How Process Mining aids in discovering, analyzing, and improving processes using event logs
- Leveraging Process Mining outputs to inform model features and automate improvement loops
- Practical example: combining Process Mining insights with AI Builder to reduce manual exceptions
Production Considerations, Governance, and Monitoring
- Data governance, privacy, and compliance requirements when using AI Builder on sensitive documents
- Model lifecycle management: retraining, versioning, and performance monitoring
- Operationalizing models with alerts, dashboards, and human-in-the-loop validation
Summary and Next Steps
Requirements
- Experience with Power Apps, Power Automate, or Power Platform administration
- Familiarity with data concepts, fundamental machine learning principles, and model evaluation techniques
- Proficiency in working with datasets, Excel/CSV exports, and basic data cleansing processes
Audience
- Power Platform developers and solution architects
- Data analysts and process owners looking to automate tasks through AI
- Business automation leaders focused on document processing and predictive use cases
14 Hours
Testimonials (2)
We did quite complex examples, so we could get a feeling of how the real work with Power Automate Desktop can look like in the real world scenario.
Michal Strnad - MicroNova AG
Course - Microsoft Flow/Power Automate
Dynamic, adaptive, and informative
