62 Neptune.ai Testimonials

"I really appreciate that I’ve never seen any outage in Neptune. And since we’re training an LLM, that it’s super critical to not have any outages in our loss curve. Other than that, there are things you often take for granted in a product: reliability, flexibility, quality of support. Neptune nails those and gives us the confidence."

"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."

"The killer feature in Neptune is custom dashboards. Without this, I wouldn’t be able to communicate my simulations to Developers, the Analytics team, and business stakeholders without any hassle. Neptune gives our Data Scientists the piece of mind that their best results won’t be lost and that communication will be a breeze."

"When working with projects that had thousands of runs, loading the interface and sorting through data was super slow in Weights & Biases. Neptune is better at handling large-scale. We’re happy with this choice."

“Neptune was easy to set up and integrate into my experimental flow. The tracking and logging options are exactly what I needed and the documentation was up to date and well written.”

“I have been pleasantly surprised with how easy it was to set up Neptune in my PyTorch Lightning projects."

"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 helped us compare our results a lot faster and a lot more efficiently."

"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."

“What we like about Neptune is that it easily hooks into multiple frameworks. Keeping track of machine learning experiments systematically over time and visualizing the output adds a lot of value for us.”

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

"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."

“This thing is so much better than Tensorboard, love you guys for creating it."

“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 set up in my situation. With Neptune, seeing training progress was as simple as hitting refresh. The feedback loop between changing the code and seeing whether anything changed is just so much shorter. Much more fun and I get to focus on what I want to do. I really wish that it existed 10 years ago when I was doing my PhD.”

"When I joined this company, we were doing quite many different experiments and it’s really hard to keep track of them all so I needed something to just view the result or sometimes or also it’s intermediate results of some experiments like what [does] the data frame look like? What [does] the CSV look like? Is it reasonable? Is there something that went wrong between the process that resulted in an undesirable result? So we were doing it manually first but just writing some log value to some log server like a Splunk."