Course Outline
Foundational Overview: AI Builder and Low-Code Artificial Intelligence
- Core functional capabilities and applicable operational use cases
- Licensing frameworks, governance policies, and tenant-level architectural considerations
- Strategic integration with the Microsoft Power Platform ecosystem, including Power Apps, Power Automate, and Dataverse
Optical Character Recognition (OCR) and Form Processing: Handling Structured and Unstructured Data
- Distinctions between predefined structured templates and variable free-form document formats
- Protocols for preparing training datasets, including field labeling, sample diversity requirements, and quality assurance standards
- Development of AI Builder form processing models and rigorous assessment of data extraction accuracy
- Post-extraction data management strategies, encompassing validation checks, normalization procedures, and robust error handling mechanisms
- Practical laboratory exercise: Executing OCR extraction across varied form types and incorporating results into automated processing workflows
Predictive Analytics: Classification and Regression Models
- Defining problem scopes: Distinguishing between qualitative (classification) and quantitative (regression) analytical tasks
- Methodologies for feature engineering and managing data integrity issues, such as missing values, within Power Platform environments
- Processes for training, testing, and interpreting key performance indicators, including accuracy, precision, recall, and root mean square error (RMSE)
- Ensuring model transparency, explainability, and adherence to fairness standards in operational contexts
- Practical laboratory exercise: Developing a custom predictive model for customer retention analysis or numerical forecasting
System Integration: Power Apps and Power Automate
- Deploying AI Builder models within both canvas and model-driven application architectures
- Designing automated workflows to process extracted data and initiate prescribed business actions
- Architectural design patterns for ensuring the scalability and long-term maintainability of AI-enabled applications
- Practical laboratory exercise: Implementing a comprehensive end-to-end scenario involving document ingestion, OCR processing, predictive analysis, and workflow automation
Adjacent Process Mining Methodologies (Optional Module)
- Utilizing Process Mining to identify, analyze, and enhance operational workflows through the examination of event logs
- Leveraging Process Mining insights to refine model inputs and establish continuous improvement cycles
- Case study application: Integrating Process Mining findings with AI Builder to minimize manual intervention and exceptions
Operational Readiness: Governance, Compliance, and Performance Monitoring
- Adherence to data governance, privacy regulations, and compliance standards when processing sensitive information using AI Builder
- Managing the full model lifecycle, including periodic retraining, version control, and continuous performance oversight
- Strategies for operationalizing models through alerting systems, performance dashboards, and human-in-the-loop verification processes
Conclusion and Future Recommendations
Requirements
- Practical experience with Power Apps, Power Automate, or the administration of the Power Platform
- Foundational knowledge of data concepts, basic machine learning principles, and standard model evaluation techniques
- Proficiency in working with datasets, including the use of Excel or CSV formats and basic data cleaning procedures
Target Audience
- Power Platform developers and enterprise solution architects
- Data analysts and process owners seeking to implement AI-driven automation
- Business automation leads responsible for document processing and predictive analytics use cases
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
