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See how AI research teams monitor and debug their large scale training faster with Neptune

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How Neptune underpins Bioptimus’ decisions in training biology foundation models

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From ablations to 100+ GPU pretraining—read how research labs use Neptune

Case study

How Neptune Underpins Bioptimus’ Decisions in Training Biology Foundation Models

Neptune is central in a lot of what we do. We rely on Neptune’s API and visualizations to compare runs, assess new model features, and revisit past versions. It underpins a lot of the decision-making of the company.
Rodolphe Jenatton
CTO at Bioptimus
Case study

How KoBold Metals Monitors 1000s of Geoscience and ML Experiments in One Place

Neptune has been critical for experiment tracking at KoBold. It gives us a transparent, searchable record of our work, something we rely on to do rigorous, applied science. And it strikes the right balance: it’s powerful and easy enough that our team actually uses it.
Liz Main
Head of Scientific Computing at KoBold Metals
Case study

How Paretos Tracks Thousands of Experiments Across Dozens of Projects With Neptune

Neptune is an important component of our system and plays a key role in internal tracking. If it were unavailable, we’d lose a valuable part of our monitoring workflow—but so far, it’s proven reliable. It just works.
Robert Haase
Lead AI Scientist at Paretos
Case study

How Navier AI uses Neptune to Rapidly Iterate on Physics Foundation Models

The ingestion just works. I start a run, open Neptune, and everything shows up how I expect it. That wasn't the case with our previous setup.
Oliver Lammas
Founding Engineer
Case study

How Neptune Helps Artera Bring AI Solutions to Market Faster

For me, Neptune is really the central place for results. If Neptune is down, I don't know how my sweep is doing.
Hans Pinckaers
ML Scientist at Artera
Case study

How BGU Research Group Tracks Thousands of Models With Neptune

We all have limited resources, even large companies. Tools like Neptune help us train fewer models by finding better models faster, optimizing our resources.
Omri Azencot
Assistant Professor at BGU
Case study

How Cradle Achieved Experiment Tracking and Data Security Goals With Self-Hosted Neptune

For us, self-hosted deployment was too difficult and time-consuming in the previous solution. We could achieve that with Neptune, and it allowed us to close important deals that had stringent security requirements.
Daniel Danciu
CTO at Cradle
Case study

How Veo Eliminated Work Loss With Neptune

Working with Neptune has brought in more structured management and enhanced security compared to our earlier approach with MLflow.
Philip Pries Henningsen
Senior ML Researcher at Veo Technologies
Case study

How Elevatus Uses Neptune to Check Experiment Results in 1 Minute

With Neptune, I have a mature observability layer to access and gain all the information. I can check any model's performance very quickly. It would take me around 1 minute to figure out this information.
Yanal Kashou
Chief Innovation Officer at Elevatus
Case study

How Brainly Avoids Workflow Bottlenecks With Automated Tracking

Neptune’s UI and the front-end work great, and you don’t feel that you ‘fight’ with it. So instead of ‘fighting’ the tool, the tool itself is helping.
Hubert Bryłkowski
Senior Machine Learning Engineer at Brainly
Case study

How Neptune Gave Waabi Organization-Wide Visibility on Experiment Data

Organic adoption by our teams has been a key indicator that the tool has added value to their workflows and they've been able to use it successfully.
Neil Isaac
Senior Staff Software Developer at Waabi
Case study

How InstaDeep No Longer Wastes Time Looking for Data With Neptune

I like that Neptune doesn't get in your way – it's not very intrusive. It also does very well with the comparison of runs, sharing, and working collaboratively.
Nicolas Lopez Carranza
DeepChain and BioAI Lead at InstaDeep

Track all your metrics in one place and debug any training issues fast