62 Neptune.ai Testimonials

"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 results right in front of me. The time we have gained back played a significant part."

“I’m working with deep learning (music information processing), previously I was using Tensorboard to track losses and metrics in TensorFlow, but now I switched to PyTorch so I was looking for alternatives and I found Neptune a bit easier to use, I like the fact that I don’t need to (re)start my own server all the time and also the logging of GPU memory etc. is nice. So far I didn’t have the need to share the results with anyone, but I may in the future, so that will be nice as well.”

“I just had a look at neptune logger after a year and to be honest, I am very impressed with the improvements in UI! Earlier, it was a bit hard to compare experiments with charts. I am excited to try this! I just had a look at neptune logger after a year and to be honest, I am very impressed with the improvements in UI! Earlier, it was a bit hard to compare experiments with charts. I am excited to try this."

"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 to the point and it’s very effective in what it does."

"Versioning jupyter notebooks is a great and unique feature."

“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 separately I would be rather discouraged. When I want to share my results I’m simply sending a link.“

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

"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."

"Clearly, handling the training of more than 7000 separate machine learning models without any specialized tool is practically impossible. We definitely needed a framework able to group and manage the experiments."

"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."

“I am super messy with my experiments, but now I have everything organized for me automatically. I love it."

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

"MLflow requires what I like to call software kung fu, because you need to host it yourself. So you have to manage the entire infrastructure — sometimes it’s good, oftentimes it’s not."

"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."