Course Outline
Introduction to AI Builder and Low-Code AI
- Overview of AI Builder capabilities and typical application scenarios
- Considerations regarding licensing, governance, and tenant-level management
- Summary of Power Platform integrations, including Power Apps, Power Automate, and Dataverse
OCR and Form Processing: Structured and Unstructured Documents
- Distinguishing between structured templates and free-form documents
- Preparing training data through field labeling, ensuring sample diversity, and adhering to quality guidelines
- Constructing an AI Builder form processing model and assessing its extraction accuracy
- Post-processing extracted data involving validation, normalization, and error management
- Practical lab: performing OCR extraction from mixed form types and integrating the results into a processing flow
Prediction Models: Classification and Regression
- Defining the problem: qualitative (classification) versus quantitative (regression) tasks
- Feature preparation and managing missing data within Power Platform workflows
- Training, testing, and interpreting model metrics such as accuracy, precision, recall, and RMSE
- Addressing model explainability and fairness in business contexts
- Practical lab: developing a custom prediction model for churn scoring or numerical forecasting
Integration with Power Apps and Power Automate
- Embedding AI Builder models into both canvas and model-driven applications
- Developing automated flows to process extracted data and initiate business actions
- Establishing design patterns for scalable and maintainable AI-driven applications
- Practical lab: executing an end-to-end scenario involving 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
- Utilizing Process Mining outputs to refine model features and automate improvement cycles
- Case study: integrating Process Mining insights with AI Builder to minimize manual exceptions
Production Considerations, Governance, and Monitoring
- Managing data governance, privacy, and compliance when applying AI Builder to sensitive documents
- The model lifecycle, including 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
- Proficiency in data concepts, fundamental ML principles, and model evaluation
- Confidence in working with datasets, Excel/CSV exports, and basic data cleansing
Audience
- Power Platform developers and solution architects
- Data analysts and process owners seeking AI-driven automation
- Business automation leads specializing in document processing and prediction 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
