Course Outline
Foundamentals of AI Builder and Low-Code AI
- Capabilities of AI Builder and typical application scenarios
- Licensing structures, governance frameworks, and tenant-specific factors
- Insight into 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 assessing extraction precision
- Processing extracted data post-extraction: validation, standardization, and error management
- Practical exercise: OCR extraction from diverse form types and embedding it into a processing workflow
Predictive Modeling: Classification and Regression Techniques
- Defining the problem: qualitative (classification) versus quantitative (regression) goals
- Preparing features and managing missing data within Power Platform workflows
- Training, testing, and analyzing model metrics such as accuracy, precision, recall, and RMSE
- Considerations for model explainability and fairness in business contexts
- Practical exercise: developing a custom prediction model for churn scoring or numerical forecasting
Integration Strategies with Power Apps and Power Automate
- Incorporating AI Builder models into canvas and model-driven applications
- Developing automated flows to handle extracted data and initiate business actions
- Design principles for scalable and maintainable AI-powered applications
- Practical exercise: a complete end-to-end scenario involving document upload, OCR, prediction, and workflow automation
Supplementary Process Mining Principles (Optional)
- The role of Process Mining in discovering, analyzing, and enhancing processes through event logs
- Leveraging Process Mining results 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 Surveillance
- Data governance, privacy, and compliance when applying AI Builder to sensitive documents
- Model lifecycle management: retraining, version control, and performance tracking
- Operationalizing models via alerts, dashboards, and human-in-the-loop verification
Recap and Future Directions
Requirements
- Proficiency with Power Apps, Power Automate, or Power Platform administration
- Understanding of data principles, fundamental machine learning concepts, and model assessment techniques
- Confidence in managing datasets, Excel/CSV exports, and foundational data cleaning tasks
Target Audience
- Power Platform developers and solution architects
- Data analysts and process owners aiming to implement AI-driven automation
- Leaders in business automation concentrating on document processing and forecasting applications
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
