Course Outline
Introduction to AI Builder and Low-Code AI
- Core capabilities of AI Builder and typical business scenarios
- Licensing, governance, and tenant-level requirements
- Overview of Power Platform integrations (Power Apps, Power Automate, Dataverse)
OCR and Form Processing: Handling Structured and Unstructured Documents
- Distinguishing between structured templates and free-form documents
- Preparing training data: field labeling, sample variety, and quality standards
- Developing an AI Builder form processing model and assessing extraction precision
- Post-processing extracted data: validation, normalization, and managing errors
- Practical lab: extracting OCR from mixed form types and integrating results into a processing workflow
Prediction Models: Classification and Regression
- Defining the problem: qualitative (classification) versus quantitative (regression) tasks
- Preparing features and managing missing data within Power Platform workflows
- Training, testing, and analyzing model metrics (accuracy, precision, recall, RMSE)
- Practical lab: creating a custom prediction model for churn/score analysis or numeric forecasting
Integration with Power Apps and Power Automate
- Establishing automated flows to process extracted data and initiate business actions
- Design patterns for building 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 refine processes through event logs
- Applying Process Mining outputs to guide model features and automate improvement cycles
- Real-world example: merging Process Mining insights with AI Builder to minimize manual exceptions
Production Readiness, Governance, and Monitoring
- Data governance, privacy, and compliance when utilizing AI Builder with sensitive documents
- Model lifecycle management: retraining, version control, and performance tracking
- Operationalizing models via alerts, dashboards, and human-in-the-loop validation
Summary and Future Directions
Requirements
- Practical experience with Power Apps, Power Automate, or Power Platform administration
- Proficiency in handling datasets, Excel/CSV exports, and foundational data cleansing
Target Audience
- Power Platform developers and solution architects
- Data analysts and process owners aiming to implement AI-driven automation
- Business automation leads specializing in document processing and predictive 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
Knowledge of the application and its uses
Jose Everardo Hernandez Esmeralda - Comercializadora NIMMKA
Course - Microsoft Power Platform Fundamentals
Machine Translated
