Course Outline
Introduction to AI Builder and Low-Code AI
- Overview of AI Builder capabilities and typical business scenarios
- Licensing, governance, and tenant-level strategic considerations
- Integration landscape within Power Platform (Power Apps, Power Automate, Dataverse)
OCR and Form Processing: Handling Structured and Unstructured Documents
- Distinguishing between structured templates and free-form documents
- Preparing training data: field labeling, sample variety, and quality standards
- Constructing AI Builder form processing models and assessing extraction precision
- Refining extracted data: validation, normalization, and managing errors
- Practical lab: Extracting data from mixed form types via OCR and integrating it into a processing flow
Prediction Models: Classification and Regression Techniques
- Defining the problem: Qualitative (classification) vs. Quantitative (regression) tasks
- Feature preparation and managing missing data within Power Platform workflows
- Training, testing, and interpreting key model metrics (accuracy, precision, recall, RMSE)
- Ensuring model explainability and fairness in business contexts
- Practical 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 building scalable, maintainable AI-driven applications
- Practical lab: End-to-end scenario involving document upload, OCR, prediction, and workflow automation
Complementary Process Mining Concepts (Optional Module)
- Utilizing Process Mining to discover, analyze, and improve processes using event logs
- Applying Process Mining outputs to enhance 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 when applying AI Builder to sensitive documents
- Managing the model lifecycle: retraining, versioning, and tracking performance
- Operationalizing models through alerts, dashboards, and human-in-the-loop validation
Summary and Future Directions
Requirements
- Proficiency with Power Apps, Power Automate, or Power Platform administration.
- Understanding of data concepts, fundamental ML principles, and model evaluation techniques.
- Ease in working with datasets, Excel/CSV exports, and basic data cleansing procedures.
Target Audience
- Power Platform developers and solution architects.
- Data analysts and process owners looking to implement AI-driven automation.
- Business automation leads focusing on document processing and prediction scenarios.
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
