Case study
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.
Case study
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.
Case study
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.
Case study
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.
Case study
At a certain stage of machine learning maturity the need for a tool like this one rises naturally. And then Neptune is a solid choice because of low entry threshold, many useful features, and good documentation and support.
Case study
The more I used Neptune, the more I felt that I would rather pay for a hosted solution than have to maintain the infrastructure myself.
Case study
My productivity in collaborating with students and also my own research speed increased dramatically. I wouldn’t know how to do my work without Neptune.
Case study
I would say the main argument for using Neptune is that you can be sure that nothing gets lost, everything is transparent, and I can always go back in history and compare.
