Edge AI Development Workflow

Edge Ai Development Workflow

Skill Profile

(Select at least one profile to enable specific modules)

• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

Overview

Edge AI Development Workflow provides a comprehensive, automated pipeline for developing, testing, and deploying AI applications on edge devices. This workflow encompasses data collection, model training, optimization, deployment, and monitoring, ensuring reliable, scalable, and maintainable edge AI solutions that meet production requirements.

Why This Matters

• Accelerated Development: Reduce time-to-market for edge AI features by 50-70%
• Quality Assurance: Ensure reliable and accurate edge deployments with automated testing
• Scalability: Deploy to thousands of edge devices efficiently with parallel deployment
• Maintainability: Simplify updates and troubleshooting with standardized workflow
• Cost Efficiency: Optimize resource usage across fleet with monitoring and alerts

Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions
• Maintain consistency across codebase
• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution
• Iterate based on feedback and requirements
• Test thoroughly before deployment

Inputs / Outputs / Contracts

• Inputs:
  • Training data (from sensors, cloud, edge devices)
  • Model architecture configuration
  • Edge device specifications and constraints
  • Deployment targets (device IDs, IPs, capabilities)
  • Monitoring and alerting configuration
• Entry Conditions:
  • Data sources configured and accessible
  • Training infrastructure available
  • Edge devices registered and accessible
  • CI/CD pipeline configured
  • Monitoring infrastructure deployed
• Outputs:
  • Trained and optimized model
  • Deployment package (model, metadata, documentation)
  • Deployment results (success/failure per device)
  • Monitoring metrics and alerts
  • Workflow execution summary
• Artifacts Required (Deliverables):
  • Trained model file
  • Compressed/optimized model
  • Deployment package
  • CI/CD pipeline configuration
  • Monitoring dashboards and alerts
• Acceptance Evidence:
  • Model accuracy > 95% of target
  • Deployment success rate > 95%
  • Monitoring coverage 100%
  • Rollback success rate > 99%
  • Workflow duration < 2 hours
• Success Criteria:
  • Model accuracy meets requirements
  • Model size fits target device constraints
  • Deployment success rate > 95%
  • Monitoring detects drift and issues
  • Rollback completes within 5 minutes

Skill Composition

• Depends on: tinyml-microcontroller-ai (Edge inference), edge-model-compression (Model optimization)
• Compatible with: on-device-model-training, hybrid-inference-architecture
• Conflicts with: None
• Related Skills: drift-detection-retraining, model-registry-versioning

Quick Start / Implementation Example

1. Review requirements and constraints
2. Set up development environment
3. Implement core functionality following patterns
4. Write tests for critical paths
5. Run tests and fix issues
6. Document any deviations or decisions

# Example implementation following best practices
def example_function():
    # Your implementation here
    pass

Assumptions / Constraints / Non-goals

• Assumptions:
  • Development environment is properly configured
  • Required dependencies are available
  • Team has basic understanding of domain
• Constraints:
  • Must follow existing codebase conventions
  • Time and resource limitations
  • Compatibility requirements
• Non-goals:
  • This skill does not cover edge cases outside scope
  • Not a replacement for formal training

Compatibility & Prerequisites

• Supported Versions:
  • Python 3.8+
  • Node.js 16+
  • Modern browsers (Chrome, Firefox, Safari, Edge)
• Required AI Tools:
  • Code editor (VS Code recommended)
  • Testing framework appropriate for language
  • Version control (Git)
• Dependencies:
  • Language-specific package manager
  • Build tools
  • Testing libraries
• Environment Setup:
  • .env.example keys: API_KEY, DATABASE_URL (no values)

Test Scenario Matrix (QA Strategy)

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

• Top Threats: Injection attacks, authentication bypass, data exposure

• Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
• Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
• Authorization: Validate user permissions before state changes

2. Performance & Resources

• Execution Efficiency: Consider time complexity for algorithms
• Memory Management: Use streams/pagination for large data
• Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

• Design Pattern: Follow SOLID principles, use Dependency Injection
• Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

• Logging Standards: Structured JSON, include trace IDs request_id
• Metrics: Track error_rate, latency, queue_depth
• Error Handling: Standardized error codes, no bare except
• Observability Artifacts:
  • Log Fields: timestamp, level, message, request_id
  • Metrics: request_count, error_count, response_time
  • Dashboards/Alerts: High Error Rate > 5%

Agent Directives & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

• Thinking Process: Analyze root cause before fixing. Do not brute-force.
• Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
• Self-Review: Check against Guardrails & Anti-patterns before finalizing.
• Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

Definition of Done (DoD) Checklist

• Tests passed + coverage met
• Lint/Typecheck passed
• Logging/Metrics/Trace implemented
• Security checks passed
• Documentation/Changelog updated
• Accessibility/Performance requirements met (if frontend)

Anti-patterns / Pitfalls

• ⛔ Don't: Log PII, catch-all exception, N+1 queries
• ⚠️ Watch out for: Common symptoms and quick fixes
• 💡 Instead: Use proper error handling, pagination, and logging

Reference Links & Examples

• Internal documentation and examples
• Official documentation and best practices
• Community resources and discussions

Versioning & Changelog

• Version: 1.0.0
• Changelog:
  • 2026-02-22: Initial version with complete template structure

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