Course Outline
Introduction to AI Builder and Low-Code AI
- Core capabilities of AI Builder and typical application scenarios
- Licensing models, governance practices, and tenant-level considerations
- Overview of integrations across the Power Platform (Power Apps, Power Automate, Dataverse)
OCR and Form Processing: Handling Structured and Unstructured Documents
- Distinctions between structured templates and free-form documents
- Preparing training data: field labeling, sample diversity, and quality standards
- Creating AI Builder form processing models and assessing extraction accuracy
- Managing extracted data through validation, normalization, and error handling
- Practical lab: extracting data from mixed form types using OCR and integrating it into a processing flow
Prediction Models: Classification and Regression
- Defining the problem: qualitative (classification) versus quantitative (regression) objectives
- Preparing features and managing missing data within Power Platform workflows
- Training, testing, and interpreting model metrics (accuracy, precision, recall, RMSE)
- Considering model explainability and fairness in business contexts
- Practical lab: building a custom prediction model for churn/score analysis or numeric forecasting
Integrating with Power Apps and Power Automate
- Embedding AI Builder models into canvas and model-driven apps
- Developing automated flows to handle extracted data and trigger business actions
- Design patterns for creating scalable and maintainable AI-driven applications
- Practical lab: 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 via event logs
- Applying Process Mining insights to refine model features and automate improvement cycles
- Practical example: leveraging Process Mining insights alongside AI Builder to minimize manual exceptions
Production Readiness, Governance, and Monitoring
- Data governance, privacy, and compliance considerations when using AI Builder on sensitive documents
- Managing the model lifecycle: retraining, versioning, and performance tracking
- Operationalizing models through alerts, dashboards, and human-in-the-loop validation
Summary and Next Steps
Requirements
- Proficiency with Power Apps, Power Automate, or Power Platform administration
- Understanding of data concepts, fundamental machine learning principles, and model evaluation techniques
- Ability to work with datasets, Excel/CSV exports, and basic data cleaning processes
Target Audience
- Power Platform developers and solution architects
- Data analysts and process owners aiming to implement AI-driven automation
- Business automation leaders specializing in 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
