Course Outline
Introduction to AI Builder and Low-Code AI
- Overview of AI Builder capabilities and typical business scenarios.
- Considerations regarding licensing, governance, and tenant-level configurations.
- A look at 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: labeling fields, ensuring sample diversity, and adhering to quality guidelines.
- Constructing an AI Builder form processing model and assessing its extraction accuracy.
- Managing post-extraction data through validation, normalization, and robust error handling.
- Hands-on lab: Performing OCR extraction on mixed form types and integrating the results into a processing flow.
Prediction Models: Classification and Regression
- Defining problem scopes: 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.
- Addressing model explainability and fairness in business contexts.
- Hands-on lab: Creating a custom prediction model for churn scoring or numeric 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.
- Designing 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)
- Utilizing Process Mining to discover, analyze, and enhance processes using event logs.
- Applying Process Mining outputs to refine model features and automate improvement cycles.
- Practical application: Merging Process Mining insights with AI Builder to minimize manual exceptions.
Production Considerations, Governance, and Monitoring
- Navigating data governance, privacy, and compliance when processing sensitive documents with AI Builder.
- Managing the model lifecycle, including 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.
- A solid grasp of fundamental data concepts, basic machine learning principles, and model evaluation techniques.
- Proficiency in working with datasets, including Excel/CSV exports and basic data cleansing procedures.
Target Audience
- Power Platform developers and solution architects.
- Data analysts and process owners aiming to integrate AI-driven automation.
- Business automation leads prioritizing 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
