“The problem with training models on remote clusters is that every time you want to see what is going on, you need to get your FTP client up, download the logs to a machine with a graphical interface, and plot it. I tried using TensorBoard but it was painful to …

"No more DevOps needed for logging. No more starting VMs just to look at some old logs. No more moving data around to compare TensorBoards."

"For our company, a big plus of Neptune is the availability of a self-hosted version. We were looking for such a solution and found that not many services offer this option at an affordable price. Using the self-hosted version of Neptune is no different from the cloud version for end …

"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."

“While logging experiments is great, what sets Neptune apart for us at the lab is the ease of sharing those logs. The ability to just send a Neptune link in slack and letting my coworkers see the results for themselves is awesome. Previously, we used Tensorboard + locally saved CSVs …

"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. …

"Neptune and Optuna go hand in hand. You should start using Neptune as early as possible to save the trouble of having to go through multiple log statements to make sense of how your model did."

"We are very integrated with AWS and want everything to happen inside of AWS, and when you are training on a large scale, you want multiple training jobs to happen at once, and that is where Neptune comes in."

"We have a mantra: always be learning. We apply this primarily to our model, which means we’re always running experiments. So me, our CEO, other people in the team—we’re constantly checking the monitoring tool. It has to be nice, smooth, and be able to handle our training data streams consistently."

"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 primarily use Neptune for training monitoring, particularly for loss tracking, which is crucial to decide whether to stop training if it’s not converging properly. It’s also invaluable for comparing experiments and presenting key insights through an intuitive dashboard to our managers and product owners."

“Neptune is making it easy to share results with my teammates. I’m sending them a link and telling what to look at, or I’m building a View on the experiments dashboard. I don’t need to generate it by myself, and everyone in my team have access to it.”

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

“I used to keep track of my models with folders on my machine and use naming conventions to save the parameters and model architecture. Whenever I wanted to track something new about the model, I would have to update the naming structure. It was painful. There was a lot of …

"Weights and Biases went from being reasonably priced to being way too much. Especially since more than half the people we wanted to be able to see our models weren’t doing modeling. When we looked for an alternative, Neptune was the only one that could offer us everything we needed."