Critical Patterns

Model Development (REQUIRED)

✅ ALWAYS: Version your models and data

from datetime import datetime

model_config = { "version": "1.2.0", "trained_at": datetime.now().isoformat(), "dataset_hash": compute_hash(training_data), "hyperparameters": {...} }

Reproducibility (REQUIRED)

✅ ALWAYS: Set seeds for reproducibility

import random import numpy as np import torch

def set_seed(seed: int = 42): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed)

Decision Tree

Need classification? → Start with simple baseline Need embeddings? → Use pre-trained models Need fine-tuning? → Start with small learning rate Need deployment? → Consider ONNX export Need monitoring? → Track drift metrics

Resources

• ML Development: ml-development.md

• Cognee Integration: cognee/