62 Neptune.ai Testimonials

“Neptune allows us to keep all of our experiments organized in a single space. Being able to see my team’s work results any time I need makes it effortless to track progress and enables easier coordination.”

"We initially aimed for a GKE deployment for our experiment tracking tool. However, the other solution we explored had a rigid installation process and limited support, making it unsuitable for our needs. Thankfully, Neptune’s on-premise installation offered the flexibility and adjustability we required. The process was well-prepared, and their engineers were incredibly helpful, answering all our questions and even guiding us through a simpler deployment approach. Neptune’s on-prem solution and supportive team saved the day, making it a win for us."

"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 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."

"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 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 has been noticably improved during the whole time I’ve been using it."

“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’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.”

“Such a fast setup! Love it:”

“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.”

"In the first month, we discussed what our ideal environment for machine learning (ML) development would look like, and experiment tracking was a key part of it."

"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."

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