Compare experiments

Compare your experiments, consistently.

With all your experiment metadata in one place, you can identify which training strategies perform best, and why. Iterate through different hypotheses with more confidence, in less time.
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icon Charts

Data shows how models behave
Comparison charts show you why

Compare loss or accuracy metrics over different epochs or data slices to see why certain models converge quickly. Or start to diverge over time.

Analyze groups of experiments based on any characteristic of a run. For example, you can contrast experiments trained on different datasets to discover which set produced better results and why.

icon Parallel coordinates

Find your optimal hyperparameters faster

Parallel Coordinates are the quickest way to know which of your many hyperparameter sets produce the best metrics across multiple runs.

icon Side-by-side view

Make better decisions about your models

Compare two strings next to each other. See single metrics side by side. Get the broad view you need to decide which models to move forward with.

icon Image comparison

Get the full picture of your experiments

Compare image metadata side by side in your Neptune dashboard.

Ideal for working on computer vision problems like classification and object detection. Or for quickly analyzing any metrics logged as images by viewing them all at the same time.

icon Artifact comparison

Spot differences in artifact data in a snap

Easily compare differences in datasets between a source and target run. Contrast their paths, size, and MD5 hash.

So you know whether your parameters or the data are behind changes in model performance.

Become more confident in your experiment results

(Like these companies)

Andreas Malekos Head of Artificial Intelligence @Continuum Industries
The ability to compare runs on the same graph is the killer feature. And being able to monitor production runs was an unexpected win that has proved invaluable.
Wojtek Rosiński Chief Technology Officer @ReSpo.Vision
We run many pipelines concurrently, so comfortably tracking each of them becomes almost impossible. Using Neptune with Kedro, we can easily track the progress of pipelines being run on many machines, and then compare the results via UI.
Patryk Miziuła Senior Data Scientist
We trained over 120K models for more than 7K subproblems. Thanks to Neptune, we could filter experiments for given subproblems and compare them to find the best one.

Easier comparisonsā†’ Quicker devā†’ Shorter path to prod