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Course Outline
Introduction to AI Builder and Low-Code AI
- Key capabilities of AI Builder and common application scenarios
- Licensing models, governance policies, and tenant-level considerations
- Overview of Power Platform integrations, including Power Apps, Power Automate, and 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
- Constructing an AI Builder form processing model and evaluating its extraction accuracy
- Post-processing of extracted data: validation, normalization, and error handling strategies
- Hands-on lab: performing OCR extraction from mixed form types and integrating results into a processing flow
Prediction Models: Classification and Regression
- Framing the problem: distinguishing between qualitative (classification) and quantitative (regression) tasks
- Feature preparation and managing missing data within Power Platform workflows
- Training, testing, and interpreting model metrics, including accuracy, precision, recall, and RMSE
- Considerations for model explainability and fairness in business contexts
- 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 both canvas and model-driven applications
- Creating automated flows to process extracted data and trigger business actions
- Design patterns for scalable and maintainable AI-driven applications
- Hands-on lab: executing an end-to-end scenario involving document upload, OCR processing, prediction, and workflow automation
Complementary Process Mining Concepts (Optional)
- Leveraging Process Mining to discover, analyze, and enhance processes using event logs
- Utilizing Process Mining outputs to inform model features and automate improvement cycles
- Practical example: combining Process Mining insights with AI Builder to minimize manual exceptions
Production Considerations, Governance, and Monitoring
- Data governance, privacy standards, and compliance requirements when using AI Builder on sensitive documents
- Managing the model lifecycle: 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 core 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 aiming to drive automation through AI
- Business automation leads focusing 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
