ML Ops Best Practices

Logging

Piotr Niedźwiedź, founder of Neptune.ai, highlights the importance of logging in machine learning, emphasizing its role in tracking the evolution of training metrics and model performance. He explains that logging in machine learning extends beyond traditional software logging by including references to code, datasets, and hyperparameters, which are crucial for understanding model development and debugging 1. Kyle Polich notes that while software engineers often use logs reactively to troubleshoot bugs, data scientists use them proactively to refine models and improve accuracy.

In machine learning, the first thing to log would be the evolution of your training metrics to understand how your training is doing.

--- Piotr Niedźwiedź

This proactive approach allows data scientists to track and resume training efficiently, especially when using cost-saving measures like spot instances 1.

   

Iterative

The iterative nature of machine learning requires a different approach to logging compared to traditional software development. Piotr Niedźwiedź explains that data scientists often run hundreds or thousands of experiments to refine models, making logging an essential tool for tracking changes and improvements 2. Unlike software development, where a plan can often be followed, machine learning involves continuous testing and adjustment to achieve desired outcomes.

You will run hundreds or thousands of experiments, of trials and you are in the debugging mode or debugging, looking for improvements.

--- Piotr Niedźwiedź

Kyle Polich adds that deploying machine learning models in production requires robust change management and monitoring systems to ensure models perform as expected, highlighting the importance of experiment tracking in MLOps 3.