62 Neptune.ai Testimonials

"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."

“Within the first few tens of runs, I realized how complete the tracking was – not just one or two numbers, but also the exact state of the code, the best-quality model snapshot stored to the cloud, the ability to quickly add notes on a particular experiment. My old methods were such a mess by comparison.”

“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.”

"We are running our training jobs through SageMaker Pipelines, and to make it reproducible, we need to log each parameter when we launch the training job with SageMaker Pipeline. A useful feature here is the `NEPTUNE_CUSTOM_RUN_ID` environment variable."

"An important detail that we considered when we decided to choose Neptune is that we can invite everybody on Neptune, even non-technical people like product managers — there is no limitation on the users. This is great because, on AWS, you’d need to get an additional AWS account, and for other experiment tracking tools, you may need to acquire a per-user license."

"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 users. However, it has a several of advantages – unlimited storage and isolation from the global network, which increases data security."

“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 approaches has notably boosted our productivity, allowing us to focus more on the experiments, develop new, good practices within our team and make better data-driven decisions. We love the fact that the integration is effortless. No matter what framework we use – it just works in the matter of minutes, allowing us to automate and unify our processes.”

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

"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."

“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."

“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.”

"So I would say the main argument for using Neptune is that you can be sure that nothing gets lost, everything is transparent, and I can always go back in history and compare."

"Neptune works flawlessly, and integrating it with PyTorch Lightning was very smooth."