Machine learning has expanded rapidly in the last few years. Instead of simple, one-directional, or linear ML pipelines, today data scientists and developers run multiple parallel experiments that can get overwhelming even for large teams. Each experiment is expected to be recorded in an immutable and reproducible format, which results in endless logs with invaluable details.

It is necessary to narrow down on techniques by comparing machine learning models thoroughly with parallel experiments. Comparing ML models is part of the broader process of tracking ML experiments. Moreover, experiment tracking is about storing all the important data and metadata, debugging model training, and, generally, analyzing the results of experiments.

Each model or any machine learning algorithm has several features that process the data in different ways. Often the data that is fed to these algorithms is also different depending on previous experiment stages. But, since machine learning teams and developers usually record their experiments, there’s ample data available for comparison. 

The challenge is to understand which parameters, data, and metadata must be considered to arrive at the final choice. It is even more challenging to understand if a parameter with a high value, say a higher metric score, actually means the model is better than one with a lower score, or if it’s only caused by statistical bias or misdirected metric design.

The comparable parameters can be divided into two high-level categories:

• development-based,
• and production-based parameters.

Comparing machine learning algorithms is important in itself, but there are some other benefits of comparing various experiments effectively, such as: 

• Better Performance
• Longer lifetime
• Easier retraining
• Speedy production