Course Outline
Introduction to AI Builder and Low-Code AI
- Core capabilities of AI Builder and typical use cases
- Licensing structures, governance frameworks, and tenant-level impacts
- Overview 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, ensuring sample variety, and adhering to quality standards
- Developing an AI Builder form processing model and assessing extraction precision
- Post-processing extracted data: implementing validation, normalization, and error management
- Practical lab: performing OCR extraction from varied form types and integrating results into a processing workflow
Prediction Models: Classification and Regression
- Defining problem scope: qualitative (classification) versus quantitative (regression) objectives
- Feature engineering and managing missing data within Power Platform workflows
- Training, testing, and analyzing model metrics such as accuracy, precision, recall, and RMSE
- Addressing model explainability and fairness in business contexts
- Practical lab: creating a custom prediction model for churn scoring or numerical forecasting
Integration with Power Apps and Power Automate
- Incorporating AI Builder models into canvas and model-driven applications
- Establishing automated flows to handle extracted data and initiate business actions
- Architectural patterns for scalable, maintainable AI-driven applications
- Practical lab: implementing an end-to-end scenario involving document upload, OCR processing, prediction, and workflow automation
Complementary Process Mining Concepts (Optional)
- Utilizing Process Mining to discover, analyze, and enhance processes via event logs
- Applying Process Mining results to refine model features and drive continuous improvement cycles
- Real-world example: combining Process Mining insights with AI Builder to minimize manual exceptions
Production Readiness, Governance, and Monitoring
- Data governance, privacy regulations, and compliance when deploying AI Builder on sensitive documents
- Managing the model lifecycle: retraining, version control, and performance tracking
- Operationalizing models through alerts, dashboards, and human-in-the-loop validation mechanisms
Conclusion and Future Directions
Requirements
- Prior hands-on experience with Power Apps, Power Automate, or Power Platform administration
- Working knowledge of data principles, fundamental Machine Learning concepts, and model evaluation methods
- Proficiency in managing datasets, Excel/CSV exports, and basic data cleansing tasks
Intended Audience
- Power Platform developers and solution architects
- Data analysts and process owners aiming to automate workflows through AI
- Business automation leaders focusing on document processing and predictive scenarios
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
