Course Outline
Introduction to AI Builder and Low-Code AI
- Overview of AI Builder capabilities and typical business scenarios.
- Key considerations regarding licensing, governance, and tenant-level setup.
- Introduction to Power Platform integrations, including Power Apps, Power Automate, and Dataverse.
OCR and Form Processing: Handling Structured and Unstructured Documents
- Understanding the distinctions between structured templates and free-form documents.
- Preparing training data, including field labeling, sample diversity, and quality standards.
- Developing an AI Builder form processing model and assessing extraction accuracy.
- Post-processing extracted data through validation, normalization, and error handling.
- Hands-on lab: Performing OCR extraction from mixed form types and integrating results into a processing flow.
Prediction Models: Classification and Regression
- Problem framing: distinguishing between qualitative (classification) and quantitative (regression) tasks.
- Feature preparation and managing 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 contexts.
- Hands-on lab: Creating 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.
- Developing automated flows to process extracted data and initiate business actions.
- Design patterns for scalable and maintainable AI-driven applications.
- Hands-on lab: An end-to-end scenario covering document upload, OCR, prediction, and workflow automation.
Complementary Process Mining Concepts (Optional)
- How Process Mining utilizes event logs to discover, analyze, and improve processes.
- Leveraging Process Mining outputs to inform model features and automate improvement cycles.
- Practical example: Combining Process Mining insights with AI Builder to minimize manual exceptions.
Production Considerations, Governance, and Monitoring
- Data governance, privacy, and compliance requirements when using AI Builder with sensitive documents.
- Managing the model lifecycle, including retraining, versioning, and performance monitoring.
- Operationalizing models through alerts, dashboards, and human-in-the-loop validation.
Summary and Next Steps
Requirements
- Practical experience with Power Apps, Power Automate, or Power Platform administration.
- Familiarity with data concepts, basic machine learning ideas, and model evaluation practices.
- Proficiency in working with datasets, Excel/CSV exports, and basic data cleansing techniques.
Target Audience
- Power Platform developers and solution architects.
- Data analysts and process owners seeking to implement 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
