Course Outline
Introduction to AI Builder and Low-Code AI
- Overview of AI Builder features and typical business use cases
- Addressing licensing, governance, and tenant-level requirements
- Examining integrations within the Power Platform ecosystem, including Power Apps, Power Automate, and Dataverse
OCR and Form Processing: Structured and Unstructured Documents
- Distinguishing between structured templates and free-form documents
- Preparing training data through field labeling, ensuring sample diversity, and adhering to quality standards
- Constructing AI Builder form processing models and assessing extraction precision
- Managing post-extraction data via validation, normalization, and error handling strategies
- Practical lab: extracting data via OCR from varied form types and embedding it into a processing workflow
Prediction Models: Classification and Regression
- Defining the problem scope for qualitative (classification) versus quantitative (regression) tasks
- Handling feature preparation and missing data within Power Platform workflows
- Training, testing, and analyzing model performance metrics such as accuracy, precision, recall, and RMSE
- Considering model explainability and fairness in business applications
- Practical lab: developing a custom prediction model for churn scores or numerical forecasting
Integration with Power Apps and Power Automate
- Incorporating AI Builder models into canvas and model-driven applications
- Establishing automated flows that process extracted data and initiate business actions
- Applying design patterns for scalable and maintainable AI-powered applications
- Practical lab: implementing an end-to-end scenario involving document upload, OCR, prediction, and workflow automation
Complementary Process Mining Concepts (Optional)
- Leveraging Process Mining to discover, analyze, and optimize processes using event logs
- Utilizing Process Mining outputs to enhance model features and automate improvement cycles
- Practical example: integrating Process Mining insights with AI Builder to minimize manual exceptions
Production Considerations, Governance, and Monitoring
- Navigating data governance, privacy, and compliance when processing sensitive documents with AI Builder
- Managing the model lifecycle through retraining, versioning, and performance tracking
- Deploying models with alerts, dashboards, and human-in-the-loop validation
Summary and Next Steps
Requirements
- Experience with Power Apps, Power Automate, or Power Platform administration
- Familiarity with data concepts, basic machine learning principles, and model evaluation
- Confidence in working with datasets, Excel/CSV exports, and basic data cleansing
Target Audience
- Power Platform developers and solution architects
- Data analysts and process owners seeking automation through AI
- Business automation leads focused on document processing and prediction 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
