Your Ultimate Guide to ML Experiment Tracking

This guide will be helpful to you if you wish to:

The purpose of the model training process is to find the best-performing hyper-parameters based on the metrics and data.

The path of discovering this arrangement entails doing several trials, assessing the results, and attempting new ideas. Consider experiment tracking to be a link between this path and the destination.

The technique of preserving all experiment-related information (metadata) that you care about for each experiment you perform is known as machine learning experiment tracking.

“What you care about” will vary based on your project needs, but it may include:

The ML Experiment Flow

In a broader sense, experiment tracking flow is a component of MLOps, a larger ecosystem of tools and approaches concerned with the operationalization of Machine Learning.

In the visualization below, for example, experiment tracking integrates the research phase of the model creation process with the deployment and monitoring phases.

Experiment tracking focuses on the iterative model development phase, where you test many different things to bring your model’s performance to the required level. A modest modification in training information can have a significant influence on performance.

You won’t be able to compare or recreate the outcomes unless you track exactly what you did. Not to mention that you’ll waste a lot of time and struggle to reach company objectives.

Experiment tracking ensures that you don’t overlook any data or insights.

You don’t have to spend hours or days deciding what experiments to run next, how your previous (or even currently running) experiments did, which experiment performed the best, or how to replicate and distribute it. You can tell by looking at your experiment tracking database. Or, at the very least, you can figure it out using only this one source of truth.

Now, let’s dig a little deeper into the various use cases and the advantages of experiment tracking.

Multiple Data, One Space

During a project, your experiment findings may wind up being spread across multiple devices, especially if several individuals are working on it. In such instances, it’s difficult to control the experimental process, and some knowledge is likely to be lost.

By design, with the experiment tracking system in place, all of your experiment findings are logged to a single repository. Your (and your ML team’s) job is simpler and, most importantly, standardized.

Compare Metrics and Parameters With Ease

The most commonly noted advantage of experiment monitoring is the ability to effortlessly compare metrics and parameters.

When you use the same technique for logging runs, the comparisons might be rather detailed. And you don’t need to do much else.

Boost Your Collaboration Flow

The possibility to exchange experiments among team members is the benefit that unites all of the abovementioned benefits.

Experiment tracking allows you to organize and compare not just your previous experiments, but also what everyone else was doing and how it worked out.

When you’re part of a team and several individuals are doing tests, having a single source of truth for your whole team is critical and makes it much easier to conclude projects.

Experiment tracking (also known as experiment management) is a component of MLOps, a wider ecosystem of tools and approaches concerned with machine learning operationalization.MLOps manages all aspects of the ML project lifecycle, including building:

That is a wide range of problems and solutions.

ML teams monitor experiments in a variety of ways, including spreadsheets, GitHub, and self-built systems. However, the most successful method is to use tools created expressly for tracking and managing machine learning projects, such as Comet.

1. Developing Spreadsheets and Naming Convention

A simple strategy is to just construct a large spreadsheet with all of the information you can fit in (metrics, variables, etc.) and a directory structure with items titled in a certain way.

You examine the findings of each trial and copy them to the spreadsheet.

In certain cases, it may be sufficient to fix your experiment tracking issues. It is not the ideal option, but it is quick and easy.

2. Relying on the Power of GitHub

Another possibility is to save all of your experiment metadata on GitHub. Though GitHub wasn’t built for machine learning, it can work in certain setups.

When conducting your experiment, you may commit stats, parameters, visualizations, and anything else you wish to keep track of to Github. It is possible to achieve this with post-commit hooks, which automatically produce or update certain files (configs, charts, etc.) when your experiment is complete.

3. Building Your Own Platform

While you can try to modify common tools to make them work for machine learning experiments, it’s always a good idea to build your own platform based on your project needs.

4. Hopping On to Comet’s Platform

Last but not the least, you can use Comet to not only track your ML experiments but also reproduce them.

Comet to not only track your ML experiments but also reproduce them.

Comet enables you to build better models quicker by utilizing cutting-edge hyperparameter optimization and supervised early stopping. Focus on providing commercial value to your data stream while we handle the rest.

Now that you know the benefits of ML experiment tracking, it’s time to implement it in your project. Here’s how to get started:

Wondering how to implement MLOps and experiment management best practices to increase the efficiency of your ML team? Download the comprehensive MLOps guide to learn more and boost your ML project performance.