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See how ML teams solve their experiment tracking problems with Neptune

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ML engineers and data scientists
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Commercial and research teams
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Projects tracked
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Philip Pries Henningsen Senior ML Researcher at Veo Technologies
Working with Neptune has brought in more structured management and enhanced security compared to our earlier approach with MLflow.
Collaboration Comparison Evaluation Tracking
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Yanal Kashou Chief Innovation Officer at Elevatus
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.
Collaboration Debugging Reproducibility Tracking
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Hubert BryƂkowski Senior Machine Learning Engineer at Brainly
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.
Collaboration Monitoring Reproducibility
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Neil Isaac Senior Staff Software Developer at Waabi
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.
Collaboration Monitoring Tracking
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Nicolas Lopez Carranza DeepChain and BioAI Lead at InstaDeep
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.
Collaboration
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Andreas Malekos Head of Artificial Intelligence at Continuum Industries
Gone are the days of writing stuff down on google docs and trying to remember which run was executed with which parameters and for what reasons. Having everything in Neptune allows us to focus on the results and better algorithms.
Collaboration Comparison Debugging Monitoring
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Viet Yen Nguyen CTO at Hypefactors
The ad-hoc techniques we used weren’t effective. At some point, everybody agreed that we could do this better. As opposed to before, we now post links to the Neptune results and it works great for us.
Collaboration
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Wojtek RosiƄski Chief Technology Officer at ReSpo.Vision
If we can choose the best-performing model, then we can save time because we would need fewer integrations to ensure high data quality. Customers are much happier because they receive higher quality data, enabling them to perform more detailed match analytics.
Collaboration Debugging Monitoring Reproducibility
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Dr. Robert Toth Founder of Theta Tech AI
We tested multiple platforms by running experiments on them. It was clear that Neptune was the right tool for us. It’s an excellent choice for users with large-scale training activities.
Collaboration Debugging Reporting
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