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Course Outline
Introduction to AI Builder and Low-Code AI
- Overview of AI Builder capabilities and common application scenarios.
- Licensing requirements, governance standards, and tenant-level considerations.
- Overview of Power Platform integrations, 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: labeling fields, ensuring sample diversity, and adhering to quality guidelines.
- Building an AI Builder form processing model and evaluating extraction accuracy.
- Post-processing extracted data: validation, normalization, and error handling strategies.
- Hands-on lab: performing OCR extraction from mixed form types and integrating it into a processing flow.
Prediction Models: Classification and Regression
- Problem framing: understanding qualitative (classification) versus quantitative (regression) tasks.
- Feature preparation and handling missing data within Power Platform workflows.
- Training, testing, and interpreting model metrics such as accuracy, precision, recall, and RMSE.
- Considerations for model explainability and fairness in business use cases.
- Hands-on lab: building a custom prediction model for churn/score analysis or numeric forecasting.
Integration with Power Apps and Power Automate
- Embedding AI Builder models into canvas and model-driven applications.
- Creating automated flows to process extracted data and trigger business actions.
- Design patterns for building scalable and maintainable AI-driven applications.
- Hands-on lab: executing an end-to-end scenario involving document upload, OCR, prediction, and workflow automation.
Complementary Process Mining Concepts (Optional)
- Understanding how Process Mining facilitates the discovery, analysis, and improvement of processes using event logs.
- Leveraging Process Mining outputs to inform model features and automate improvement loops.
- Practical example: combining Process Mining insights with AI Builder to reduce manual exceptions.
Production Considerations, Governance, and Monitoring
- Data governance, privacy, and compliance requirements when using AI Builder on sensitive documents.
- Managing the model lifecycle: retraining, versioning, and performance monitoring.
- Operationalizing models through alerts, dashboards, and human-in-the-loop validation.
Summary and Next Steps
Requirements
- Prior experience with Power Apps, Power Automate, or Power Platform administration.
- Familiarity with data concepts, fundamental machine learning principles, and model evaluation methods.
- Comfort working with datasets, Excel/CSV exports, and basic data cleansing techniques.
Audience
- Power Platform developers and solution architects.
- Data analysts and process owners aiming to achieve automation through AI.
- Business automation leads focused on document processing and prediction use cases.
14 Hours
Testimonials (2)
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
