Course Outline
Introduction to AI Builder and the Low-Code AI Landscape
- Key capabilities of AI Builder and typical application 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 high-quality training data through field labeling, sample diversity, and adherence to quality guidelines.
- Constructing an AI Builder form processing model and assessing extraction precision.
- Post-processing extracted data through validation, normalization, and robust error handling.
- Hands-on lab: extracting data via OCR from mixed form types and integrating the results into a processing workflow.
Predictive Models: Mastering Classification and Regression
- Framing the problem: contrasting qualitative (classification) and 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 scoring or numeric forecasting.
Integrating with Power Apps and Power Automate
- Embedding AI Builder models into both canvas and model-driven applications.
- Building automated flows to process extracted data and initiate business actions.
- Design patterns for creating 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 Module)
- Utilizing Process Mining to discover, analyze, and enhance processes through event logs.
- Applying Process Mining outputs to refine model features and automate improvement cycles.
- Practical example: integrating Process Mining insights with AI Builder to minimize manual exceptions.
Production Readiness, Governance, and Monitoring
- Navigating data governance, privacy, and compliance when processing sensitive documents with AI Builder.
- Managing the model lifecycle through retraining, version control, and performance tracking.
- Operationalizing models using alerts, dashboards, and human-in-the-loop validation mechanisms.
Summary and Strategic Next Steps
Requirements
- Proficiency in Power Apps, Power Automate, or general Power Platform administration.
- Understanding of core data concepts, fundamental machine learning principles, and model assessment techniques.
- Confidence in managing datasets, handling Excel/CSV exports, and performing basic data cleansing.
Target Audience
- Power Platform developers and solution architects.
- Data analysts and process owners aiming to drive automation through AI.
- Business automation leaders focused 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
