ai-ml-integration-workflow

Prompt file imported from DashZeroAlionSystems/LightDom (.windsurf/workflows/ai-ml-integration-workflow.md). Copyright stays with the author.

1. Research Phase

   • Activate automated-research-trigger.md for ML task detection
   • Research current state-of-the-art ML techniques and frameworks
   • Identify integration opportunities with existing LightDom features
   • Document findings in neural-network-best-practices-research memory

2. Requirements Analysis

   • Define ML use cases within LightDom architecture
   • Determine data sources and preprocessing requirements
   • Identify model deployment and serving needs
   • Establish performance and accuracy requirements

3. Architecture Design

   • Design neural network architectures for specific use cases
   • Plan data pipelines and feature engineering workflows
   • Design model training and evaluation pipelines
   • Plan integration with existing LightDom services

4. Component Development

   • Create specialized ML dashboard components (ModelCard, MetricsChart, NeuralNetworkVisualizer)
   • Implement training progress monitoring interfaces
   • Build model management and deployment controls
   • Develop data visualization components for ML insights

5. Implementation Phase

   • Implement neural network training scripts and pipelines
   • Develop model serving and inference APIs
   • Create data preprocessing and feature engineering modules
   • Implement model evaluation and monitoring systems

6. Integration Testing

   • Test ML components with existing design system
   • Validate performance and user experience
   • Ensure compatibility with current LightDom architecture
   • Perform security and privacy assessments

7. Deployment Preparation

   • Set up model deployment pipelines
   • Configure monitoring and alerting systems
   • Prepare documentation and training materials
   • Plan rollout strategy and user adoption

8. Continuous Learning

   • Establish feedback loops for model improvement
   • Monitor performance metrics and user satisfaction
   • Update research triggers based on usage patterns
   • Plan next iteration of ML enhancements

9. Documentation & Memory

   • Update all relevant memories with implementation insights
   • Document ML integration patterns for future reference
   • Create usage examples and best practices guides
   • Establish maintenance and update procedures