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Course Outline
Introduction to AI Builder and Low-Code AI
- Overview of AI Builder capabilities and common application 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 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: performing OCR extraction from mixed form types and integrating it 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: building a custom prediction model for churn/score prediction 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: executing an end-to-end scenario — document upload, OCR, prediction, and workflow automation.
Complementary Process Mining Concepts (Optional)
- How Process Mining facilitates the discovery, analysis, and improvement of 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 when utilizing AI Builder on sensitive documents.
- Model lifecycle management: retraining, versioning, and performance monitoring.
- Operationalizing models through alerts, dashboards, and human-in-the-loop validation.
Summary and Next Steps
Requirements
- Prior 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 methods.
Audience
- Power Platform developers and solution architects.
- Data analysts and process owners aiming to achieve automation through AI.
- Business automation leads 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
