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Course Outline
Introduction to AI Builder and Low-Code AI
- AI Builder capabilities and common 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
- Differences between structured templates and free-form documents.
- Preparing training data: labeling fields, ensuring sample diversity, and adhering to 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 between qualitative (classification) and quantitative (regression) tasks.
- Feature preparation and handling missing data within Power Platform workflows.
- Training, testing, and interpreting model metrics (accuracy, precision, recall, RMSE).
- Model explainability and fairness considerations in business use cases.
- Hands-on lab: build a custom prediction model for churn/score or numeric forecast.
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 apps.
- Hands-on lab: end-to-end scenario — document upload, OCR, prediction, and workflow automation.
Complementary Process Mining Concepts (Optional)
- How Process Mining helps discover, analyze, and improve processes using event logs.
- Using Process Mining outputs to inform model features and automate improvement loops.
- Practical example: combine Process Mining insights with AI Builder to reduce manual exceptions.
Production Considerations, Governance, and Monitoring
- Data governance, privacy, and compliance when using AI Builder on sensitive documents.
- Model lifecycle: 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, basic machine learning principles, and model evaluation.
- Comfort working with datasets, Excel/CSV exports, and basic data cleansing techniques.
Audience
- Power Platform developers and solution architects.
- Data analysts and process owners seeking automation through AI.
- Business automation leads focused on document processing and prediction 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
