Course Outline
Introduction to AI Builder and Low-Code AI
- Core capabilities of AI Builder and typical use cases.
- Licensing, governance, 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: labelling fields, ensuring sample diversity, and adhering to quality guidelines.
- Constructing an AI Builder form processing model and assessing extraction accuracy.
- Post-processing extracted data: validation, normalisation, and error management.
- Practical lab: performing OCR extraction from mixed form types and integrating results into a processing flow.
Prediction Models: Classification and Regression
- Defining the problem: qualitative (classification) versus quantitative (regression) tasks.
- Preparing features 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.
- Practical lab: developing a custom prediction model for churn/score analysis or numerical forecasting.
Integration with Power Apps and Power Automate
- Embedding AI Builder models into canvas and model-driven apps.
- Designing automated flows to process extracted data and trigger business actions.
- Design patterns for scalable and maintainable AI-driven applications.
- Practical lab: an end-to-end scenario covering document upload, OCR, prediction, and workflow automation.
Complementary Process Mining Concepts (Optional)
- How Process Mining assists in discovering, analysing, and improving processes using event logs.
- Leveraging Process Mining outputs to refine model features and automate improvement cycles.
- Practical example: combining Process Mining insights with AI Builder to minimise 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
- Practical experience with Power Apps, Power Automate, or Power Platform administration.
- Familiarity with data concepts, fundamental machine learning principles, and model evaluation techniques.
- Confidence in handling datasets, working with Excel/CSV exports, and performing basic data cleaning.
Target Audience
- Power Platform developers and solution architects.
- Data analysts and process owners looking to implement AI-driven automation.
- Business automation leads with a focus on document processing and prediction use cases.
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized 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
