Course Outline
Introduction to AI Builder and Low-Code AI
- Core capabilities of AI Builder and typical application scenarios
- Licensing structures, governance policies, and tenant-level requirements
- Understanding the ecosystem: integration with Power Apps, Power Automate, and Dataverse
OCR and Form Processing: Handling Structured and Unstructured Documents
- Distinguishing between fixed templates and free-form documentation
- Preparing training datasets: field labeling, ensuring sample variety, and adhering to quality standards
- Developing AI Builder form processing models and assessing extraction precision
- Refining extracted data through validation, normalization, and robust error handling
- Practical lab: extracting data via OCR from diverse form types and incorporating it into processing workflows
Prediction Models: Classification and Regression Techniques
- Defining the problem: distinguishing between qualitative (classification) and quantitative (regression) objectives
- Feature engineering and managing missing data within Power Platform environments
- Training, validating, and interpreting key model metrics including accuracy, precision, recall, and RMSE
- Considering model interpretability and fairness in real-world business applications
- Practical lab: creating a custom prediction model for churn scoring or numerical forecasting
Integration with Power Apps and Power Automate
- Incorporating AI Builder models into both canvas and model-driven applications
- Building automated flows that process extracted data to initiate business actions
- Applying design patterns for scalable and maintainable AI-enhanced applications
- Practical lab: an end-to-end scenario involving document upload, OCR processing, prediction, and workflow automation
Supplementary Process Mining Concepts (Optional)
- How Process Mining utilizes event logs to discover, analyze, and enhance business processes
- Leveraging Process Mining results to refine model features and drive automated improvement cycles
- Case study: merging Process Mining insights with AI Builder to minimize manual exceptions
Production Readiness, Governance, and Monitoring
- Managing data governance, privacy, and compliance when applying AI Builder to sensitive documents
- Managing the model lifecycle: retraining strategies, version control, and performance tracking
- Operationalizing models through alerts, dashboards, and human-in-the-loop validation mechanisms
Recap and Future Directions
Requirements
- Proficiency in Power Apps, Power Automate, or the administration of the Power Platform.
- Knowledge of fundamental data concepts, introductory machine learning principles, and model evaluation techniques.
- Practical experience managing datasets, Excel/CSV exports, and performing basic data cleansing.
Target Audience
- Power Platform developers and solution architects.
- Data analysts and process managers aiming to implement AI-driven automation.
- Business automation leaders prioritizing document processing and predictive analytics 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
