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name: machine-learning description: Comprehensive machine learning development with training, evaluation, and deployment capabilities. Use when training models, developing ML pipelines, or deploying machine learning systems. version: 2.0.0 tier: gold author: SPARC System tags:

• ml
• deep-learning
• training
• evaluation
• deployment
• data-science dependencies:
• python-specialist
• agentdb-learning
• ml-expert agents:
• ml-developer
• data-steward
• evaluator related_skills:
• when-debugging-ml-training-use-ml-training-debugger
• when-developing-ml-models-use-ml-expert
• agentdb-learning
• holistic-evaluation
• baseline-replication category: platforms

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When NOT to Use This Skill

• Simple data preprocessing without model training
• Statistical analysis that does not require ML models
• Rule-based systems without learning components
• Operations that do not involve model training or inference

Success Criteria

• Model training convergence: Loss decreasing consistently
• Validation accuracy: Meeting or exceeding baseline targets
• Training time: Within expected bounds for dataset size
• GPU utilization: >80% during training
• Model export success: 100% successful saves
• Inference latency: <100ms for real-time applications

Edge Cases & Error Handling

• GPU Memory Overflow: Reduce batch size, use gradient accumulation, or mixed precision
• Divergent Training: Implement learning rate scheduling, gradient clipping
• Data Pipeline Failures: Validate data integrity, handle missing/corrupted files
• Version Mismatches: Lock dependency versions, use containerization
• Checkpoint Corruption: Save multiple checkpoints, validate before loading
• Distributed Training Failures: Handle node failures, implement fault tolerance

Guardrails & Safety

• NEVER train on unvalidated or uncleaned data
• ALWAYS validate model outputs before deployment
• ALWAYS implement reproducibility (random seeds, version pinning)
• NEVER expose training data in model artifacts or logs
• ALWAYS monitor for bias and fairness issues
• ALWAYS implement model versioning and rollback capabilities

Evidence-Based Validation

• Verify hardware availability: Check GPU/TPU status before training
• Validate data quality: Run data integrity checks and statistics
• Monitor training: Track loss curves, gradients, and metrics
• Test model performance: Evaluate on held-out test set
• Benchmark inference: Measure latency and throughput under load

Machine Learning Development Skill

Complete workflow for machine learning model development, training, evaluation, and deployment.

When to Use

Auto-trigger when detecting:

• "train model", "machine learning", "ML pipeline"
• "deep learning", "neural network", "model training"
• "data preprocessing", "feature engineering"
• "model evaluation", "hyperparameter tuning"
• "model deployment", "ML ops"

Capabilities

1. Data Pipeline

• Data preprocessing and cleaning
• Feature engineering and selection
• Data augmentation
• Train/validation/test splitting
• Data versioning with DVC

2. Model Training

• Neural network architectures
• Hyperparameter optimization
• Transfer learning
• Distributed training
• Training monitoring and logging

3. Model Evaluation

• Multi-metric evaluation
• Cross-validation
• Confusion matrices and ROC curves
• Fairness and bias detection
• Performance benchmarking

4. Model Deployment

• Model serialization and versioning
• API endpoint creation
• Containerization
• Monitoring and logging
• A/B testing support

Agent Workflow

// Auto-spawned agents for ML development
Task("ML Researcher", "Research SOTA models and best practices for [task]", "researcher")
Task("Data Engineer", "Preprocess data and engineer features", "coder")
Task("ML Developer", "Implement and train model architecture", "ml-developer")
Task("Model Evaluator", "Evaluate model performance and fairness", "evaluator")
Task("ML Ops Engineer", "Deploy model with monitoring", "coder")

Integration Points

• AgentDB Learning: Reinforcement learning algorithms
• ML Expert: Advanced model development
• Holistic Evaluation: Comprehensive evaluation metrics
• Data Steward: Dataset documentation and bias auditing
• Deployment Readiness: Production ML deployment

Resources

• resources/scripts/model-trainer.py - Complete training pipeline
• resources/scripts/data-preprocessor.py - Data preprocessing utilities
• resources/scripts/model-evaluator.js - Evaluation framework
• resources/scripts/ml-pipeline.sh - End-to-end ML pipeline
• resources/templates/training-config.yaml - Training configuration
• resources/templates/model-architecture.json - Model architecture definitions
• resources/templates/evaluation-metrics.yaml - Evaluation metrics configuration

Examples

See examples/ directory:

• model-training.py - Complete model training workflow
• data-pipeline.py - Data preprocessing and feature engineering
• model-deployment.py - Model deployment with FastAPI

Testing

Run tests with:

pytest tests/

Performance

• Supports distributed training with PyTorch DDP
• GPU acceleration for training and inference
• Automated hyperparameter tuning with Optuna
• Model versioning and experiment tracking with MLflow