Course Outline
Introduction to AI Builder and Low-Code AI
- Core AI Builder capabilities and typical application scenarios.
- Licensing requirements, governance frameworks, and tenant-level factors.
- 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 labelling, sample diversity, and quality standards.
- Developing an AI Builder form processing model and assessing extraction accuracy.
- Post-processing extracted data: validation, normalisation, and error management.
- Practical lab: OCR extraction from diverse form types and integration into a processing flow.
Predictive Models: Classification and Regression
- Defining the problem: qualitative (classification) versus quantitative (regression) tasks.
- Preparing features and managing missing data within Power Platform workflows.
- Training, testing, and interpreting model metrics such as accuracy, precision, recall, and RMSE.
- Model explainability and fairness considerations in business contexts.
- Practical lab: creating a custom predictive model for churn scoring or numerical forecasting.
Integration with Power Apps and Power Automate
- Embedding AI Builder models into canvas and model-driven apps.
- Developing automated flows to process extracted data and trigger business actions.
- Design patterns for scalable and maintainable AI-driven applications.
- Practical lab: an end-to-end scenario covering document upload, OCR, prediction, and workflow automation.
Supplementary Process Mining Concepts (Optional)
- Utilising Process Mining to discover, analyse, and improve processes via event logs.
- Leveraging Process Mining outputs to inform model features and drive improvement cycles.
- Practical example: combining Process Mining insights with AI Builder to minimise manual exceptions.
Production Readiness, Governance, and Monitoring
- Data governance, privacy, and compliance considerations when using AI Builder on sensitive documents.
- Model lifecycle management: retraining, versioning, and performance monitoring.
- Operationalising 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 ideas, and model evaluation techniques.
- Proficiency in handling datasets, Excel/CSV exports, and basic data cleansing.
Target Audience
- Power Platform developers and solution architects.
- Data analysts and process owners looking to leverage AI for automation.
- Business automation leaders concentrating 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
