Course Outline
Introduction to AI Builder and Low-Code AI
- Overview of AI Builder capabilities and typical business scenarios
- Key considerations regarding licensing, governance, and tenant-level setup
- Snapshot 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, ensuring sample diversity, and adhering to quality guidelines
- Constructing an AI Builder form processing model and assessing extraction accuracy
- Post-processing extracted data: implementing validation, normalization, and error handling strategies
- Hands-on lab: performing OCR extraction from mixed form types and integrating results into a processing flow
Prediction Models: Classification and Regression
- Defining the problem scope: qualitative (classification) versus quantitative (regression) tasks
- Preparing features and managing missing data within Power Platform workflows
- Training, testing, and interpreting key model metrics such as accuracy, precision, recall, and RMSE
- Considering model explainability and fairness in business contexts
- Hands-on lab: developing a custom prediction model for churn/score analysis or numeric forecasting
Integration with Power Apps and Power Automate
- Embedding AI Builder models into canvas and model-driven applications
- Creating automated flows to process extracted data and initiate business actions
- Applying design patterns for scalable and maintainable AI-driven applications
- Hands-on lab: executing an end-to-end scenario involving document upload, OCR, prediction, and workflow automation
Complementary Process Mining Concepts (Optional)
- How Process Mining utilizes event logs to discover, analyze, and improve processes
- Leveraging Process Mining outputs to inform model features and drive automated improvement loops
- Practical example: combining Process Mining insights with AI Builder to minimize manual exceptions
Production Considerations, Governance, and Monitoring
- Addressing data governance, privacy, and compliance when applying AI Builder to sensitive documents
- Managing the model lifecycle: retraining, versioning, and performance monitoring
- Operationalizing models through alerts, dashboards, and human-in-the-loop validation
Summary and Next Steps
Requirements
- Practical experience with Power Apps, Power Automate, or Power Platform administration
- Familiarity with core data concepts, fundamental ML principles, and model evaluation techniques
- Proficiency in working with 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 leads specializing in 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
