Course Outline
Introduction to AI Builder and Low-Code AI
- Core capabilities of AI Builder and typical application scenarios
- Licensing frameworks, governance policies, and tenant-level implications
- Overview of integrations within the Power ecosystem (Power Apps, Power Automate, Dataverse)
OCR and Form Processing: Managing Structured and Unstructured Documents
- Distinguishing between structured templates and free-form documentation
- Preparing training datasets: field labelling, ensuring sample diversity, and adhering to quality standards
- Constructing an AI Builder form processing model and measuring extraction precision
- Post-extraction data handling: validation, standardisation, and error management
- Practical lab: performing OCR extraction from diverse form types and integrating results into a processing pipeline
Predictive Modelling: Classification and Regression
- Defining the problem: qualitative (classification) versus quantitative (regression) objectives
- Feature engineering 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: developing a custom prediction model for churn scoring or numerical forecasting
Integration with Power Apps and Power Automate
- Embedding AI Builder models into both canvas and model-driven applications
- Designing automated flows to process extracted data and initiate business actions
- Architectural patterns for creating scalable and maintainable AI-driven applications
- Practical lab: executing an end-to-end scenario involving document upload, OCR processing, prediction, and workflow automation
Supplementary Process Mining Concepts (Optional)
- How Process Mining facilitates the discovery, analysis, and optimisation of processes using event logs
- Leveraging Process Mining outputs to refine model features and automate improvement cycles
- Real-world example: combining Process Mining insights with AI Builder to minimise manual exceptions
Production Readiness, Governance, and Monitoring
- Data governance, privacy, and compliance standards when utilising AI Builder with sensitive documents
- Model lifecycle management: retraining, version control, and performance tracking
- Operationalising models through alerts, dashboards, and human-in-the-loop verification
Recap and Future Directions
Requirements
- Proficiency in Power Apps, Power Automate, or general Power Platform administration
- Familiarity with core data concepts, foundational machine learning principles, and model evaluation techniques
- Confidence in managing datasets, Excel/CSV exports, and performing basic data cleaning
Target Audience
- Power Platform developers and solution architects
- Data analysts and process owners looking to implement AI-driven automation
- Business automation leaders with a focus 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
Dynamic, adaptive, and informative
