Course Outline
AI Builder Fundamentals and Low-Code AI
- Overview of AI Builder capabilities and typical business scenarios
- Discussion on licensing, governance, and tenant-level factors
- Review 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, including field labeling, ensuring sample diversity, and adhering to quality standards
- Developing an AI Builder form processing model and assessing extraction accuracy
- Managing post-processing steps for extracted data, such as validation, normalization, and error management
- Practical lab: Extracting data via OCR from mixed form types and integrating it into a processing workflow
Predictive Models: Classification and Regression Strategies
- Defining 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 like accuracy, precision, recall, and RMSE
- Considering model explainability and fairness in business contexts
- Practical lab: Creating a custom prediction model for churn scoring or numerical forecasting
Integrating with Power Apps and Power Automate
- Incorporating AI Builder models into both canvas and model-driven applications
- Developing automated flows to handle extracted data and initiate business actions
- Exploring design patterns for scalable and maintainable AI-driven applications
- Practical lab: Executing an end-to-end scenario involving document upload, OCR, prediction, and workflow automation
Supplementary Process Mining Concepts (Optional)
- How Process Mining utilizes event logs to discover, analyze, and improve processes
- Applying Process Mining results to refine model features and automate improvement cycles
- Case study: Combining Process Mining insights with AI Builder to minimize manual exceptions
Production Readiness, Governance, and Monitoring
- Addressing data governance, privacy, and compliance when processing sensitive documents with AI Builder
- Managing the model lifecycle, including retraining, version control, and performance monitoring
- Operationalizing models through alerts, dashboards, and human-in-the-loop verification
Recap and Future Directions
Requirements
- Practical experience with Power Apps, Power Automate, or Power Platform administration
- Working knowledge of data concepts, fundamental machine learning principles, and model evaluation techniques
- Proficiency 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 implement AI-driven automation
- Business automation leaders 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
