Course Outline
Introduction to AI Builder and Low-Code AI
- Core capabilities of AI Builder and typical application scenarios
- Considerations regarding licensing, governance, and tenant-level management
- Overview of integrations across the Power Platform, including Power Apps, Power Automate, and Dataverse
OCR and Form Processing: Handling Structured and Unstructured Documents
- Distinguishing between structured templates and free-form document layouts
- Preparing training data: field labeling, ensuring sample diversity, and adhering to quality standards
- Developing an AI Builder form processing model and assessing extraction precision
- Processing extracted data post-extraction: implementing validation, normalization, and error handling mechanisms
- Practical lab: Performing OCR extraction from mixed form types and integrating the results into a processing workflow
Predictive Modeling: Classification and Regression
- Defining the problem scope: qualitative tasks (classification) versus quantitative tasks (regression)
- Feature engineering and managing missing data within Power Platform environments
- Training, testing, and interpreting key model metrics such as accuracy, precision, recall, and RMSE
- Addressing model explainability and fairness within 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 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 processing, prediction, and workflow automation
Supplementary Process Mining Concepts (Optional)
- Leveraging Process Mining to discover, analyze, and refine processes using event logs
- Utilizing Process Mining outputs to enhance model features and automate improvement cycles
- Case study: Combining Process Mining insights with AI Builder to minimize manual exceptions
Production Readiness, Governance, and Monitoring
- Addressing data governance, privacy, and compliance when applying AI Builder to sensitive documents
- Managing the model lifecycle: strategies for retraining, versioning, and performance tracking
- Operationalizing models through alerts, dashboards, and human-in-the-loop validation
Recap and Future Pathways
Requirements
- Prior hands-on experience with Power Apps, Power Automate, or general Power Platform administration
- A solid understanding of fundamental data concepts, basic machine learning principles, and model assessment
- Proficiency in managing datasets, handling Excel/CSV exports, and performing basic data cleansing
Target Audience
- Power Platform developers and solution architects
- Data analysts and process owners looking to leverage AI for automation
- Business automation leaders with a focus on 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
