ml

name	ml
description	This skill should be used when working with machine learning models — architecture review, training pipeline design, feature engineering, and deployment guidance. Use when: - "review this ML model" - "design ML training pipeline" - "how should I deploy this model" - "feature engineering advice" - "ML architecture guidance"
phase_relevance	["design","build"]
archetype_relevance	["*"]

ML Engineering Skill

Guide machine learning model development, training, and deployment.

Quick Start

Review Model Architecture

/wicked-garden:data:ml review path/to/model/

Reviews: Model choice, training data quality, evaluation strategy, deployment readiness.

Design Training Pipeline

/wicked-garden:data:ml pipeline --type classification

Generates: Data loading, feature engineering, training config, evaluation framework.

ML Development Workflow

1. Problem Definition

• Problem type clear (classification/regression/ranking)
• Success metric defined
• Baseline established
• Data availability confirmed
• Inference requirements understood (latency/throughput)

2. Data Assessment

• Sufficient volume (>1000x features)
• Labels accurate and consistent
• Features available at inference time
• No data leakage from target
• Class balance acceptable

3. Feature Engineering

Good features are: Predictive, Available at inference, Clean (no leakage), Interpretable.

Common transformations:

• Numeric: Scaling, log transform
• Categorical: One-hot, target encoding
• Time: Extract components, cyclical encoding
• Aggregations: Rolling windows, user stats

4. Model Selection

Data Size	Structured	Recommendation
<10K rows	Yes	Linear/Simple tree
10K-1M	Yes	GradientBoosting (XGBoost/LightGBM)
>1M	Yes	Deep learning possible
Any	Images/Text	Deep learning

5. Training & Evaluation

Split strategy: Random (if i.i.d.), Time-based (if time series), Cross-validation (robust).

Key metrics:

• Classification: Accuracy, Precision, Recall, F1, AUC
• Regression: RMSE, MAE, R²

6. Hyperparameter Tuning

• Grid search: Exhaustive, slow
• Random search: More efficient
• Bayesian optimization: Most efficient

7. Deployment

Patterns: Batch scoring, REST API, Streaming

Checklist:

• Inference latency acceptable
• Model size appropriate
• Feature pipeline reproducible
• Monitoring instrumented
• Rollback plan documented

ML Monitoring

Model Performance: Prediction accuracy, distribution shifts, error rate by segment.

Data Quality: Feature distributions, missing rates, cardinality changes.

System Health: Latency (p50, p95, p99), throughput, memory.

Integration

• wicked-brain:search: Find model code with wicked-brain:search "model|classifier" (FTS5 over indexed code)
• Native tasks: Track experiments via TaskCreate with metadata.event_type="task"
• wicked-garden:data:analyze: Analyze training data via DuckDB SQL

Best Practices

• Always baseline: Start simple, measure improvement, justify complexity
• Avoid leakage: Use only past data, split before processing
• Monitor production: Track predictions, detect drift, plan retraining
• Document everything: Architecture, features, training data, results

Reference

For detailed techniques:

• Output Templates - Model review report, model card