Course Outline
Introduction to AI Builder and Low-Code AI
- Core capabilities of AI Builder and typical application scenarios
- Licensing models, governance frameworks, and tenant-level implications
- Snapshot of Power Platform integrations, including Power Apps, Power Automate, and 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
- Constructing an AI Builder form processing model and measuring extraction precision
- Refining extracted data: validation, standardization, and error management
- Practical lab: performing OCR extraction from mixed document types and embedding it into a processing workflow
Prediction Models: Classification and Regression
- Defining the problem: qualitative (classification) versus quantitative (regression) objectives
- Preparing features and managing missing data within Power Platform workflows
- Training, testing, and analyzing model performance metrics (accuracy, precision, recall, RMSE)
- Addressing model transparency and fairness in business contexts
- Practical lab: developing a custom prediction model for churn/score analysis or numerical forecasting
Integration with Power Apps and Power Automate
- Embedding AI Builder models into canvas and model-driven applications
- Establishing automated flows to process extracted data and initiate business actions
- Design patterns for scalable, sustainable AI-driven applications
- Practical lab: a complete scenario covering document upload, OCR, prediction, and workflow automation
Supplementary Process Mining Concepts (Optional)
- Leveraging Process Mining to discover, analyze, and enhance processes via event logs
- Utilizing Process Mining outputs to refine 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 processing sensitive documents with AI Builder
- Model lifecycle management: retraining, version control, and performance tracking
- Operationalizing models through alerts, dashboards, and human-in-the-loop validation
Conclusion and Future Directions
Requirements
- Practical experience with Power Apps, Power Automate, or administering the Power Platform
- Familiarity with data fundamentals, introductory machine learning concepts, and model assessment techniques
- Proficiency in handling datasets, Excel/CSV exports, and basic data cleaning procedures
Intended Audience
- Power Platform developers and solution architects
- Data analysts and process owners aiming to drive automation through AI
- Business automation leaders 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
Dynamic, adaptive, and informative
