Course Outline
Overview of AI Builder and Low-Code AI
- Core capabilities of AI Builder and typical application scenarios.
- Licensing frameworks, governance standards, and tenant-level factors.
- Synopsis of Power Platform integrations, including Power Apps, Power Automate, and Dataverse.
OCR and Form Processing: Handling Structured and Unstructured Documents
- Distinctions between fixed 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 precision.
- Managing post-extraction data: validation, normalization, and error management.
- Practical lab: Executing OCR extraction from diverse form types and incorporating it into a processing workflow.
Predictive Modeling: Classification and Regression Techniques
- Defining the problem: Qualitative (classification) versus quantitative (regression) objectives.
- Preparing features and managing missing data within Power Platform workflows.
- Training, testing, and analyzing 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 numerical forecasting.
Integration with Power Apps and Power Automate
- Embedding AI Builder models into both canvas and model-driven applications.
- Establishing automated flows to process extracted data and initiate business actions.
- Design strategies for building scalable and maintainable AI-driven applications.
- Practical lab: A complete scenario involving document upload, OCR processing, prediction, and workflow automation.
Supplementary Process Mining Concepts (Optional)
- How Process Mining utilizes event logs to discover, analyze, and enhance business processes.
- Leveraging Process Mining outputs to refine model features and automate improvement cycles.
- Real-world example: Merging Process Mining insights with AI Builder to minimize manual exceptions.
Production Readiness, Governance, and Monitoring
- Data governance, privacy, and compliance requirements when using AI Builder for sensitive documents.
- Managing the model lifecycle: retraining, version control, and performance tracking.
- Implementing models with alerts, dashboards, and human-in-the-loop validation.
Recap and Future Directions
Requirements
- Prior experience managing Power Apps, Power Automate, or the broader Power Platform.
- A solid understanding of data concepts, fundamental machine learning principles, and model evaluation techniques.
- Proficiency in handling datasets, working with Excel or CSV exports, and performing basic data cleansing.
Target Audience
- Developers and solution architects specializing in the Power Platform.
- Data analysts and process stakeholders looking to leverage AI for automation.
- Business automation leaders concentrated 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
