"We have all the metrics in our shared file storage as a backup, but we don’t really have a nice way to access them, to sort them, etc. We don’t have a setup for it because Neptune has been stable enough for us not to need it."

"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 a minute to figure out this information. I don’t have to go deeper and waste a lot of time. I have the …

"At some point, one of my students tried doing the tracking process manually, and he was very frustrated after one project. Any manual change can mess up information organization and how you track it. And if you do not build it well, then you suffer, you need to recode, etc. …

"As our company has grown from a startup to a sizeable organization of 200 people, robust security and effective user management have become increasingly evident and vital."

“Without information I have in the Monitoring section I wouldn’t know that my experiments are running 10 times slower as they could. All of my experiments are being trained on separate machines which I can access only via ssh. If I would need to download and check all of this …

“I didn’t expect this level of support.”

"I’ve used Neptune from 2019, first for my personal projects and now within the company. During this time, I saw changes and improvements in UI, but also performance and reliability. But at the same time, I always appreciated that it never became too cluttered with too many things. It’s straight …

"We’ve got a few teams across different countries and different time zones and prior to Neptune, we were just shipping each other zips of like TensorBoard logs, so being able to see it all in space and it’s all just logged to the central area is really great and has …

"Neptune made sense to us due to its pay-per-use or usage-based pricing. Now when we are doing active experiments then we can scale up and when we’re busy integrating all our models for a few months that we scale down again."

"We use Neptune for most of our tracking tasks, from experiment tracking to uploading the artifacts. A very useful part of tracking was monitoring the metrics, now we could easily see and compare those F-scores and other metrics."

"We tried MLflow. But the problem is that they have no user management features, which messes up a lot of things."

"Building something like a power line is a huge project, so you have to get the design right before you start. The more reasonable designs you see, the better decision you can make. Optioneer can get you design assets in minutes at a fraction of the cost of traditional design …

"We initially aimed for a GKE deployment for our experiment tracking tool. However, the other solution we explored had a rigid installation process and limited support, making it unsuitable for our needs. Thankfully, Neptune’s on-premise installation offered the flexibility and adjustability we required. The process was well-prepared, and their engineers …

"We use PyTorch Lightning, and it was just a matter of changing the tracker from Weights and Biases to Neptune. It’s like two lines of code. It’s actually quite easy."

"Self-hosted deployment for ML solutions will become more and more important. People don't feel comfortable with valuable intellectual property being stored in 3rd party DBs. For us, such deployment was too difficult and time-consuming in the previous solution. We could achieve that with Neptune, and it allowed us to close …