Code Best Practices

1. Separate Concerns

Keep data processing, model training, evaluation, and inference in separate modules:

project/
├── data/
│   ├── preprocessing.py
│   ├── feature_engineering.py
│   └── validation.py
├── models/
│   ├── architecture.py
│   ├── training.py
│   └── inference.py
├── evaluation/
│   ├── metrics.py
│   └── validation.py
└── config/
    └── config.yaml

2. Configuration Management

Use configuration files (YAML, JSON) for hyperparameters, paths, and settings:

• Never hardcode hyperparameters in code
• Use environment-specific configs (dev, staging, prod)
• Version control all configuration files
• Validate configs at startup with schema validation

3. Reproducibility

• Set Random Seeds: Always set seeds for random number generators (NumPy, PyTorch, TensorFlow)
• Version Dependencies: Pin exact versions in requirements.txt or poetry.lock
• Data Versioning: Track which dataset version was used for training
• Environment Capture: Use tools like Docker or conda to capture exact environments

4. Experiment Tracking

Log all experiments with metadata:

• Hyperparameters and configuration
• Model architecture and parameters
• Training metrics (loss, accuracy, etc.)
• Data versions and splits
• Code version (Git commit hash)
• Environment details (OS, Python version, GPU info)

1. Data Validation

• Validate data schemas before processing
• Check for missing values, outliers, and data drift
• Implement data quality checks in pipelines
• Fail fast on invalid data rather than silently continuing

2. Avoid Data Leakage

• Split data before any preprocessing
• Fit scalers/transformers only on training data
• Never use future information to predict past events
• Be careful with time-series cross-validation
• Validate that test set is truly unseen

3. Memory Efficiency

• Use generators/iterators for large datasets
• Load data in batches rather than all at once
• Use appropriate data types (float32 vs float64)
• Clear intermediate variables when not needed

1. Model Architecture

• Keep models modular and composable
• Separate model definition from training logic
• Use factory patterns for model creation
• Document architectural decisions and trade-offs

2. Training Code

• Implement checkpointing for long-running training
• Add early stopping to prevent overfitting
• Log training progress and metrics regularly
• Handle GPU/CPU fallback gracefully
• Make training resumable from checkpoints

3. Inference Code

• Optimize inference for production (batch processing, quantization)
• Implement proper error handling and fallbacks
• Add input validation and sanitization
• Log predictions for monitoring and debugging
• Support both batch and single predictions