“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.”

"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."

"Weights and Biases went from being reasonably priced to being way too much. Especially since more than half the people we wanted to be able to see our models weren’t doing modeling. When we looked for an alternative, Neptune was the only one that could offer us everything we needed."

"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 …

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

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

“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 …

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

"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."

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

"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 …