Course Outline
Introduction to AI Builder and Low-Code AI
- Core capabilities of AI Builder and typical application scenarios
- Licensing models, governance standards, and tenant-level considerations
- Overview of integrations across the Power Platform (Power Apps, Power Automate, Dataverse)
OCR and Form Processing: Handling Structured and Unstructured Documents
- Distinctions between structured templates and free-form documents
- Preparing training data: field labeling, sample diversity, and quality standards
- Constructing an AI Builder form processing model and assessing extraction accuracy
- Post-processing extracted data: validation, normalization, and error management
- Practical lab: OCR extraction from diverse form types and integration into a processing workflow
Prediction Models: Classification and Regression
- Defining problems: qualitative (classification) versus quantitative (regression) tasks
- Feature preparation and managing missing data within Power Platform workflows
- Training, testing, and interpreting model metrics (accuracy, precision, recall, RMSE)
- Model interpretability and fairness considerations in business contexts
- Practical lab: developing a custom prediction model for churn/scoring or numeric forecasting
Integration with Power Apps and Power Automate
- Embedding AI Builder models into canvas and model-driven apps
- Developing automated flows to handle extracted data and initiate business actions
- Design patterns for scalable and maintainable AI-driven applications
- Practical lab: end-to-end scenario covering document upload, OCR, prediction, and workflow automation
Complementary Process Mining Concepts (Optional)
- Utilizing Process Mining to discover, analyze, and enhance processes using event logs
- Leveraging Process Mining outputs to refine 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, and compliance when employing AI Builder on sensitive documents
- Model lifecycle: retraining, version control, and performance monitoring
- Operationalizing models with alerts, dashboards, and human-in-the-loop validation
Conclusion and Future Directions
Requirements
- Hands-on experience with Power Apps, Power Automate, or Power Platform administration
- Basic understanding of data concepts, fundamental machine learning principles, and model assessment
- Proficiency in working with datasets, Excel/CSV exports, and elementary data cleansing
Target Audience
- Power Platform developers and solution architects
- Data analysts and process owners looking to automate workflows through AI
- Business automation leads specializing in document processing and forecasting use cases
Testimonials (3)
Practical and hands on labs on report developmemt using Power BI The labs were excellent and the trainer offered very good hands on sessions
Sinzala Sichaanji - Bank of Zambia
Course - Mastering Power Platform: Power Apps, Power Automate, DataVerse, Power BI, and Power Virtual Agents
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
