"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."

"I like the dashboards because we need several metrics, so you code the dashboard once, have those styles, and easily see them on one screen. Then, any other person can view the same thing, so that’s pretty nice."

"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 …

"Neptune’s UI is highly configurable, which is way better than MLflow."

"My productivity in collaborating with students and also my own research speed increased dramatically. I wouldn’t know how to do my work without Neptune."

“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.”

"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."

"We evaluated several commercial and open-source solutions. We looked at the features for tracking experiments, the ability to share, the quality of the documentation, and the willingness to add new features. Neptune was the best choice for our use cases."

“While logging experiments is great, what sets Neptune apart for us at the lab is the ease of sharing those logs. The ability to just send a Neptune link in slack and letting my coworkers see the results for themselves is awesome. Previously, we used Tensorboard + locally saved CSVs …

“I had been thinking about systems to track model metadata and it occurred to me I should look for existing solutions before building anything myself. Neptune is definitely satisfying the need to standardize and simplify tracking of experimentation and associated metadata. My favorite feature so far is probably the live …

"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."

"We have all the metrics in our shared file storage as a backup, but we don’t really have a nice way to access them, to sort them, etc. We don’t have a setup for it because Neptune has been stable enough for us not to need 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 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 …