Course Outline
Introduction to AI Builder and Low-Code AI
- Core capabilities of AI Builder and common application scenarios
- Licensing, governance, and tenant-level considerations
- Overview of integrations across the Power Platform (Power Apps, Power Automate, Dataverse)
OCR and Form Processing: Structured and Unstructured Documents
- Distinguishing between structured templates and free-form documents
- Preparing training data: field labelling, sample diversity, and quality guidelines
- Constructing an AI Builder form processing model and assessing extraction accuracy
- Post-processing extracted data: validation, normalisation, and error handling
- Hands-on lab: OCR extraction from mixed form types and integration into a processing flow
Prediction Models: Classification and Regression
- Problem framing: qualitative (classification) versus quantitative (regression) tasks
- Feature preparation and managing missing data within Power Platform workflows
- Training, testing, and interpreting model metrics (accuracy, precision, recall, RMSE)
- Model explainability and fairness considerations in business contexts
- Hands-on lab: building a custom prediction model for churn scoring or numerical forecasting
Integration with Power Apps and Power Automate
- Embedding AI Builder models into canvas and model-driven applications
- Creating automated flows to process extracted data and trigger business actions
- Design patterns for scalable and maintainable AI-driven applications
- Hands-on lab: end-to-end scenario involving document upload, OCR, prediction, and workflow automation
Complementary Process Mining Concepts (Optional)
- How Process Mining aids in discovering, analysing, and improving processes using event logs
- Utilising Process Mining outputs to inform model features and automate improvement loops
- Practical example: combining Process Mining insights with AI Builder to reduce manual exceptions
Production Considerations, Governance, and Monitoring
- Data governance, privacy, and compliance when using AI Builder on sensitive documents
- Model lifecycle management: retraining, versioning, and performance monitoring
- Operationalising models through alerts, dashboards, and human-in-the-loop validation
Summary and Next Steps
Requirements
- Experience with Power Apps, Power Automate, or general Power Platform administration
- Familiarity with data concepts, fundamental machine learning principles, and model evaluation
- Confidence in working with datasets, Excel/CSV exports, and basic data cleansing techniques
Target Audience
- Power Platform developers and solution architects
- Data analysts and process owners looking to drive automation through AI
- Business automation leads with a focus on document processing and prediction use cases
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customised Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from £3200 + VAT*
Contact us for an exact quote and to hear our latest promotions
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
