Course Outline
Introduction to AI Builder and Low-Code AI
- Core capabilities of AI Builder and typical application scenarios.
- Insights into licensing, governance, and tenant-level requirements.
- Overview of integrations across the Power Platform ecosystem (Power Apps, Power Automate, Dataverse).
OCR and Form Processing: Handling Structured and Unstructured Documents
- Distinguishing between structured templates and free-form documents.
- Preparing high-quality training data, including field labelling, sample diversity, and quality standards.
- Constructing an AI Builder form processing model and assessing extraction accuracy.
- Refining extracted data through validation, normalisation, and robust error handling.
- Practical session: Extracting data via OCR from mixed form types and integrating the output into a processing flow.
Prediction Models: Classification and Regression Techniques
- Defining the problem: contrasting qualitative (classification) tasks with quantitative (regression) tasks.
- Preparing features and managing missing data within Power Platform workflows.
- Training, testing, and interpreting key model metrics such as accuracy, precision, recall, and RMSE.
- Considering model explainability and fairness in the context of business applications.
- Practical session: 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.
- Developing automated flows to process extracted data and initiate business actions.
- Applying design patterns to ensure AI-driven applications are scalable and maintainable.
- Practical session: Executing an end-to-end scenario involving document upload, OCR, prediction, and workflow automation.
Supplementary Process Mining Concepts (Optional)
- How Process Mining leverages event logs to discover, analyse, and improve processes.
- Utilising Process Mining outputs to refine model features and automate improvement cycles.
- Practical example: Integrating Process Mining insights with AI Builder to minimise manual exceptions.
Production Readiness, Governance, and Monitoring
- Addressing data governance, privacy, and compliance when using AI Builder on sensitive documents.
- Managing the model lifecycle, including retraining, versioning, and performance monitoring.
- Operationalising models through alerts, dashboards, and human-in-the-loop validation.
Summary and Future Directions
Requirements
- Practical experience with Power Apps, Power Automate, or Power Platform administration.
- A working understanding of data concepts, fundamental machine learning principles, and model evaluation techniques.
- Proficiency in managing datasets, Excel/CSV exports, and basic data cleaning tasks.
Target Audience
- Power Platform developers and solution architects.
- Data analysts and process owners aiming to implement AI-driven automation.
- Business automation leaders prioritising document processing and predictive 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 2600 € + 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
