Advanced ML Coding Tactics: Streamline Experiments Like a Pro

Once you’ve trained a few models, the bottleneck shifts to experiment management. Tracking what you tried, what worked, and why it worked becomes the real challenge.

📁 1. Create Modular Code for Reuse

• Move preprocessing steps into functions or classes
• Use a config system (like Hydra or YAML) for parameters
• Abstract model definitions from training loops

📋 2. Track Experiments With Tools

• MLflow: Log metrics, hyperparameters, artifacts
• Weights & Biases: Collaborate and visualize training across runs
• DVC: Version control for data and models

📈 3. Evaluate Model Performance Strategically

• Use k-fold cross-validation with consistent random seeds
• Balance metrics: accuracy, precision, recall, F1, AUC
• Use confusion matrices for multi-class insight

📤 4. Keep Your Work Shareable

• Use Jupyter notebooks for exploration, .py scripts for production
• Write README files with usage instructions and model goals
• Push results to GitHub, Hugging Face, or Colab

Modern ML success depends on clarity, not just code. Track, document, and refine to scale faster with confidence.