Course Outline
Introduction to AI Builder and Low-Code AI
- Overview of AI Builder capabilities and typical application scenarios.
- Considerations regarding licensing, governance, and tenant-level management.
- Introduction to 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 standards.
- Constructing an AI Builder form processing model and assessing extraction accuracy.
- Post-processing extracted data: implementing validation, normalization, and error handling mechanisms.
- Practical lab: Performing OCR extraction from mixed form types and integrating the results into a processing flow.
Prediction Models: Classification and Regression
- Defining problem scope: 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.
- Addressing model explainability and fairness in business contexts.
- Practical lab: Developing a custom prediction model for churn scoring or numeric forecasting.
Integration with Power Apps and Power Automate
- Embedding AI Builder models into both canvas and model-driven apps.
- Developing automated flows to process extracted data and initiate business actions.
- Practical 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 improve processes through event logs.
- Applying Process Mining outputs to refine model features and automate improvement cycles.
- Case study: Combining Process Mining insights with AI Builder to minimize manual exceptions.
Production Considerations, Governance, and Monitoring
- Managing the model lifecycle: retraining, version control, and performance monitoring.
- Operationalizing models through alerts, dashboards, and human-in-the-loop validation.
Summary and Future Steps
Requirements
- Practical experience with Power Apps, Power Automate, or Power Platform administration.
- Proficiency in handling datasets, working with Excel/CSV exports, and performing basic data cleansing.
Target Audience
- Power Platform developers and solution architects.
- Data analysts and process owners aiming to implement AI-driven automation.
- Business automation leaders with a focus on document processing and prediction 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
