Monitor model training

Time is your most important asset. Maximize it with real-time monitoring.

Stop waiting hours for training to end only to realize your model diverged quickly — and you could have stopped it sooner. Save time and resources with instant feedback on your experiments.
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icon Model performance monitoring

Better models start with better visibility

Get constant insight into the state of your training from a live feed of your models’ performance.

  • Save resources by stopping training early when models start to diverge
  • Get better insight into model behavior by watching metrics as they evolve
  • Make training more responsive by tweaking hyperparameters or training  strategies on the fly if something looks off
icon Hardware consumption monitoring

Get the most out of your machines

Eliminate bottlenecks in your training by monitoring hardware consumption throughout your experiments.

  • Ensure your resources run with maximum efficiency by monitoring usage in real-time
  • Prevent crashes by adjusting usage when memory, GPU, or other resources get close to their limits 
  • Scale your resources smarter by seeing the effects of changing your model or data on your consumption

Get unprecedented visibility into your experiments

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Hubert Brylkowski Senior Machine Learning Engineer @Brainly
Neptune gives us excellent insight on simple data processing jobs — not just training. Because we can monitor the usage of resources — even when we use all cores of the machines. In a few lines of code, we have much better visibility.
Michał Kardas Machine Learning Researcher @TensorCell
Without the information in Neptune’s monitoring dashboard, I wouldn’t know my experiments run 10 times slower than they should. All of my experiments are being trained on separate machines which I can access only via ssh. If I needed to download and check all of this separately, I would be rather discouraged.
Kha Nguyen Senior Data Scientist @Zoined
I didn’t think about logging CPU metrics or memory metrics when I used MLflow. But it turned out to be pretty important for debugging something running in parallel with big data. So this is something that I find extremely helpful with Neptune.

Get the insights you need to build better models faster at your fingertips