Course Outline
Introduction to AI Builder and Low-Code AI
- Core capabilities of AI Builder and typical business scenarios
- Licensing models, governance structures, and tenant-level configurations
- Overview of integrations with 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 standards
- Developing an AI Builder form processing model and assessing extraction accuracy
- Processing extracted data: implementing validation, normalization, and robust error handling
- Practical lab: performing OCR extraction from mixed form types and integrating results into a processing workflow
Prediction Models: Classification and Regression
- Defining problem statements: qualitative (classification) versus quantitative (regression) objectives
- Feature engineering and managing missing data within Power Platform workflows
- Training, testing, and interpreting key 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 scores or numeric forecasting
Integrating with Power Apps and Power Automate
- Embedding AI Builder models into canvas and model-driven apps
- Developing automated flows to process extracted data and initiate business actions
- Design patterns for scalable and maintainable AI-driven applications
- Practical lab: implementing an end-to-end scenario involving document upload, OCR, prediction, and workflow automation
Complementary Process Mining Concepts (Optional)
- How Process Mining utilizes event logs to discover, analyze, and enhance processes
- Utilizing Process Mining outputs to refine model features and automate improvement cycles
- Real-world example: combining Process Mining insights with AI Builder to minimize manual exceptions
Production Readiness, Governance, and Monitoring
- Data governance, privacy, and compliance considerations when processing sensitive documents with AI Builder
- Managing the model lifecycle: retraining, version control, and performance monitoring
- Operationalizing models through alerts, dashboards, and human-in-the-loop validation
Summary and Future Directions
Requirements
- Practical experience with Power Apps, Power Automate, or general Power Platform administration
- A solid grasp of data concepts, foundational machine learning principles, and model evaluation techniques
- Proficiency in handling datasets, Excel or CSV exports, and basic data cleaning procedures
Target Audience
- Power Platform developers and solution architects
- Data analysts and process owners looking to implement AI-driven automation
- 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
