Model Training

Model Training

Skill Profile

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

Overview

Model training is the process of teaching machine learning models to make predictions or decisions based on data. This skill covers comprehensive training workflows including pipeline design, data preparation, training loops, hyperparameter tuning, experiment tracking, checkpoint management, early stopping, learning rate scheduling, distributed training, and model evaluation.

Why This Matters

• Model Quality: Proper training ensures better models
• Reproducibility: Consistent training workflows
• Efficiency: Optimized training saves time and resources
• Experimentation: Systematic hyperparameter exploration
• Production: Reliable training for deployment

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 (train, validation, test)
  • Model architecture
  • Training configuration (hyperparameters, epochs, batch size)
  • Checkpoint directory
• Entry Conditions:
  • Data is properly preprocessed and split
  • Model architecture is defined
  • Training configuration is validated
  • Compute resources (GPU) are available
• Outputs:
  • Trained model
  • Training metrics (loss, accuracy, etc.)
  • Checkpoints
  • Best model
  • Experiment logs
• Artifacts Required (Deliverables):
  • Trained model file (.pt, .pth)
  • Training metrics logs
  • Checkpoint files
  • Configuration files
  • Training script
• Acceptance Evidence:
  • Training loss curve showing convergence
  • Validation metrics meeting target thresholds
  • Checkpoint files saved successfully
  • Best model loaded and verified
• Success Criteria:
  • Training loss converges to stable value
  • Validation metrics meet or exceed targets (e.g., accuracy > 90%)
  • No overfitting (train/val gap < 5%)
  • Training completes within expected time

Skill Composition

• Depends on:
  • 05-ai-ml-core/data-augmentation
  • 05-ai-ml-core/data-preprocessing
• Compatible with:
  • 05-ai-ml-core/model-optimization
  • 77-mlops-data-engineering/mlflow-patterns
• Conflicts with: None
• Related Skills:
  • 05-ai-ml-core/model-optimization
  • 77-mlops-data-engineering/mlflow-patterns

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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