Course Outline
Overview of AI Builder and Low-Code AI
- Core capabilities of AI Builder and typical application scenarios.
- Key considerations regarding licensing, governance, and tenant-level setup.
- Introduction to Power Platform integrations, including Power Apps, Power Automate, and Dataverse.
OCR and Form Processing: Handling Structured and Unstructured Documents
- Distinguishing between structured templates and free-form documents.
- Preparing training data: labeling fields, ensuring sample diversity, and adhering to quality standards.
- Constructing an AI Builder form processing model and assessing its extraction accuracy.
- Managing extracted data: validation, normalization, and error management strategies.
- Practical lab: performing OCR extraction from mixed form types and integrating the results into a processing workflow.
Predictive Models: Classification and Regression
- Defining the problem: differentiating between qualitative (classification) and quantitative (regression) tasks.
- Preparing features and addressing 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 business contexts.
- Practical lab: developing a custom prediction model for churn scoring or numerical forecasting.
Integrating with Power Apps and Power Automate
- Incorporating AI Builder models into both canvas and model-driven applications.
- Designing automated flows that process extracted data and initiate business actions.
- Implementing design patterns for scalable and maintainable AI-driven applications.
- Practical lab: executing an end-to-end scenario involving document upload, OCR, prediction, and workflow automation.
Integrating Process Mining Concepts (Optional)
- Utilizing Process Mining to discover, analyze, and enhance processes using event logs.
- Applying Process Mining insights to refine model features and automate improvement cycles.
- Case study: combining Process Mining findings with AI Builder to minimize manual exceptions.
Production Readiness, Governance, and Monitoring
- Ensuring data governance, privacy, and compliance when processing sensitive documents with AI Builder.
- Managing the model lifecycle, including retraining, version control, and performance tracking.
- Operationalizing models through alerts, dashboards, and human-in-the-loop verification.
Recap and Future Directions
Requirements
- Hands-on experience with Power Apps, Power Automate, or managing the Power Platform environment.
- A solid understanding of data concepts, fundamental machine learning principles, and model assessment techniques.
- Proficiency in working with datasets, handling Excel/CSV exports, and performing basic data cleaning tasks.
Target Audience
- Power Platform developers and solution architects.
- Data analysts and process owners looking to implement automation through AI capabilities.
- Business automation professionals focused on document processing and predictive scenarios.
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
