“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 …

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

"Our ML teams at Waabi continuously run large-scale experiments with ML models. A significant challenge we faced was keeping track of the data they collected from experiments and exporting it in an organized and shareable way."

"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."

“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 …

"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 …

"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."

"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."

"I used Weights & Biases before Neptune. It’s impressive at the beginning, it works out of the box, and the UI is quite nice. But during the four years I used it, it didn’t improve —they didn’t fully develop the features they were working on. So I appreciate that Neptune …

“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.”

“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.”

“Neptune provides an accessible and intuitive way to visualize, analyze and share metrics of our projects. We can not only discuss it with other team members, but also with management, in a way that can be easily interpreted by someone not familiar with the implementation details. Tracking and comparing different …

“The last few hours have been my first w/ Neptune and I’m really appreciative of how much time it’s saved me not having to fiddle w/ matplotlib in addition to everything else.“

"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 …