Course Outline
Introduction to AI Builder and Low-Code AI Solutions
- Overview of AI Builder capabilities and applicable use cases for federal operations
- Licensing structures, governance frameworks, and tenant-level configuration requirements
- Integration with the Power Platform ecosystem, including Power Apps, Power Automate, and Dataverse
Optical Character Recognition (OCR) and Document Processing: Structured and Unstructured Data
- Distinctions between structured template-based forms and free-form documents
- Training data preparation: field labeling, sample diversity, and quality assurance standards
- Development of AI Builder form processing models and evaluation of extraction precision
- Post-processing protocols: data validation, normalization, and error management
- Practical exercise: OCR extraction from varied document types and integration into automated workflows for government applications
Predictive Analytics: Classification and Regression Models
- Problem definition: qualitative classification versus quantitative regression tasks
- Feature engineering and handling missing data within Power Platform workflows
- Model training, testing, and interpretation of performance metrics (accuracy, precision, recall, RMSE)
- Model interpretability and fairness assessments in agency contexts
- Practical exercise: development of a custom prediction model for attrition analysis or numeric forecasting for government use
Integration with Power Apps and Power Automate
- Deployment of AI Builder models into canvas and model-driven applications
- Configuration of automated flows to process extracted data and initiate business processes
- Architectural patterns for scalable and maintainable AI-enabled applications
- Practical exercise: end-to-end implementation—document ingestion, OCR processing, prediction, and workflow automation
Complementary Process Mining Concepts (Optional)
- Utilization of process mining to discover, analyze, and optimize operational processes via event logs
- Application of process mining outputs to inform model features and automate continuous improvement cycles
- Case study: leveraging process mining insights with AI Builder to minimize manual exceptions in government workflows
Production Deployment, Governance, and Monitoring
- Data governance, privacy safeguards, and compliance requirements when processing sensitive documents with AI Builder for government entities
- Model lifecycle management: retraining protocols, version control, and performance monitoring
- Operationalizing models through alerts, dashboards, and human-in-the-loop validation mechanisms
Summary and Recommended Next Steps
Requirements
- Professional experience administering Power Apps, Power Automate, or the broader Power Platform infrastructure
- Working knowledge of foundational data principles, machine learning concepts, and model assessment methodologies
- Proficiency in managing datasets, exporting data via Excel or CSV formats, and executing preliminary data cleaning procedures
Target Audience
- Power Platform developers and solution architects responsible for system design and implementation
- Data analysts and process owners aiming to leverage artificial intelligence for operational automation
- Business automation leads addressing specific use cases related to document processing and predictive analytics, tailored for government agencies
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
