Course Outline
Introduction to AI Builder and Low-Code AI
- Core capabilities of AI Builder and typical application scenarios
- Licensing structures, governance frameworks, and tenant-level factors
- Overview of integration points within the Power Platform (Power Apps, Power Automate, Dataverse)
OCR and Form Processing: Handling Structured and Unstructured Documents
- Distinguishing between fixed templates and free-form documents
- Preparing training datasets: field labeling, sample variety, and quality standards
- Developing an AI Builder form processing model and assessing extraction precision
- Managing post-extraction data: validation, normalization, and error management
- Practical lab: extracting data via OCR from diverse form types and integrating it into a processing workflow
Predictive 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 key model metrics (accuracy, precision, recall, RMSE)
- Considerations for model interpretability 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 process extracted data and initiate business actions
- Design patterns for building scalable, maintainable AI-driven applications
- Practical lab: an end-to-end scenario involving document upload, OCR processing, prediction, and workflow automation
Complementary Process Mining Concepts (Optional)
- Utilizing Process Mining to discover, analyze, and enhance processes through event logs
- Leveraging Process Mining outputs to guide model features and automate improvement cycles
- Case study: combining Process Mining insights with AI Builder to minimize manual exceptions
Production Readiness, Governance, and Monitoring
- Data governance, privacy, and compliance standards when using AI Builder on sensitive documents
- Managing the model lifecycle: 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 management
- Understanding of data fundamentals, introductory machine learning concepts, and model assessment techniques
- Proficiency in managing datasets, Excel/CSV exports, and basic data cleaning procedures
Target Audience
- Power Platform developers and solution architects
- Data analysts and process leaders looking to leverage AI for automation
- Business automation heads specializing in document processing and predictive 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
