Course Outline
Introduction to AI Builder and Low-Code AI
- Core capabilities of AI Builder and common application scenarios
- Licensing structures, governance frameworks, and tenant-level considerations
- 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 diversity, and adhering to quality guidelines
- Constructing an AI Builder form processing model and evaluating extraction accuracy
- Post-processing extracted data: validation, normalization, and robust error handling
- Practical lab: Extracting data via OCR from mixed form types and integrating results into a processing flow
Prediction Models: Classification and Regression
- Framing the problem: differentiating between qualitative (classification) and quantitative (regression) tasks
- Feature preparation and managing missing data within Power Platform workflows
- Training, testing, and interpreting key model metrics such as accuracy, precision, recall, and RMSE
- Considering model explainability and fairness in business contexts
- Practical lab: Creating a custom prediction model for churn scoring or numeric forecasting
Integration with Power Apps and Power Automate
- Embedding AI Builder models into both canvas and model-driven apps
- Developing automated flows to process extracted data and trigger subsequent business actions
- Design patterns for building scalable and maintainable AI-driven applications
- Comprehensive lab: An end-to-end scenario covering document upload, OCR processing, prediction, and workflow automation
Complementary Process Mining Concepts (Optional)
- Leveraging Process Mining to discover, analyze, and improve processes using event logs
- Utilizing Process Mining outputs to refine model features and automate improvement loops
- Practical example: Combining Process Mining insights with AI Builder to minimize manual exceptions
Production Readiness, Governance, and Monitoring
- Data governance, privacy, and compliance considerations when using AI Builder on sensitive documents
- Managing the model lifecycle: retraining, version control, and performance monitoring
- Operationalizing models through alerts, dashboards, and human-in-the-loop validation
Summary and Future Steps
Requirements
- Practical experience with Power Apps, Power Automate, or general Power Platform administration
- A solid grasp of data concepts, fundamental machine learning principles, and model evaluation techniques
- Proficiency in handling datasets, managing Excel/CSV exports, and performing basic data cleansing
Target Audience
- Power Platform developers and solution architects
- Data analysts and process owners looking to drive automation through AI
- Business automation leads 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
