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Neptune makes it simple and fast to query, filter, and extract experiment data at scale In 2 minutes, you’ll get an idea of how neptune can support the monitoring of foundation model training and debugging of any issues that might arise. Mission we want to give ai researchers the same level of control and confidence when training models as software developers have when shipping applications
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The story 2016 prototype built during a kaggle competition Here’s a short overview of neptune’s key capabilities for tracking foundation model training Explore documentation for neptune, the most scalable experiment tracker for teams that train foundation models.
Backed by $18m in funding and included in cb insights’ “top 100 ai startups” list in 2021 and 2022, neptune helps teams monitor, evaluate, and scale model training with confidence
Blog for ml/ai practicioners with articles about llmops You'll find here guides, tutorials, case studies, tools reviews, and more. Neptune’s visualization charts show you every anomaly in your experiments — even if you log 1,000,000 data points to a single experiment That’s why we charge based on the volume of data you store
When you log more data, our costs go up because we store, index, and optimize it for fast access So you’re paying for what you actually use, not how long your runs take This model is predictable, scalable, and tied directly to the value you get from neptune. Neptune.ai is an experiment tracker designed with a strong focus on collaboration and scalability
Join 1000s of researchers, professors, students, and kagglers using neptune to make monitoring experiments, comparing runs, and sharing results far easier than with open source tools.
