Course Outline
Introduction to AI Builder and Low-Code AI
- Key capabilities of AI Builder and typical application scenarios
- Insights into licensing, governance, and tenant-level implications
- Overview of Power Platform integrations, including 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, sample diversity, and quality standards
- Constructing an AI Builder form processing model and assessing extraction precision
- Post-processing extracted data: validation, normalization, and error management
- Practical lab: extracting OCR data from mixed form types and integrating it into a processing workflow
Prediction Models: Classification and Regression
- Defining the problem: qualitative (classification) versus quantitative (regression) objectives
- Preparing features and managing missing data within Power Platform workflows
- Training, testing, and interpreting model metrics such as accuracy, precision, recall, and RMSE
- Considering model explainability and fairness in business contexts
- Practical lab: creating a custom prediction model for churn scoring or numerical forecasting
Integration with Power Apps and Power Automate
- Embedding AI Builder models into canvas and model-driven applications
- Developing automated flows to handle extracted data and initiate business actions
- Design patterns for creating scalable and maintainable AI-driven applications
- Practical lab: a complete end-to-end scenario involving document upload, OCR processing, prediction, and workflow automation
Complementary Process Mining Concepts (Optional)
- How Process Mining aids in discovering, analyzing, and improving processes through event logs
- Utilizing Process Mining outputs to guide model features and automate improvement cycles
- Practical example: leveraging Process Mining insights alongside AI Builder to minimize manual exceptions
Production Considerations, Governance, and Monitoring
- Data governance, privacy, and compliance when applying AI Builder to sensitive documents
- Model lifecycle management: retraining, version control, and performance tracking
- Operationalizing models through alerts, dashboards, and human-in-the-loop validation
Summary and Future Directions
Requirements
- Practical experience with Power Apps, Power Automate, or Power Platform administration
- Familiarity with data concepts, fundamental machine learning principles, and model evaluation techniques
- Proficiency in handling datasets, Excel/CSV exports, and basic data cleaning processes
Target Audience
- Power Platform developers and solution architects
- Data analysts and process owners looking to leverage AI for automation
- Business automation leaders focused 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
Knowledge of the application and its uses
Jose Everardo Hernandez Esmeralda - Comercializadora NIMMKA
Course - Microsoft Power Platform Fundamentals
Machine Translated
