Neptune
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Description:
Neptune is an MLOps stack component for experiment tracking. It allows users to track, compare, and share their models in one place. Neptune is used by scaling ML teams to skip days of debugging disorganized models, avoid long and messy model handovers, and start logging for free.
For Tasks:
For Jobs:
Features
- Log model metadata from anywhere in your pipeline
- See and compare results in the web app
- Cut down the time you spend debugging
- Monitor training live
- Develop production-ready models quicker
Advantages
- Any framework
- Any metadata type
- Advanced comparison
- Live training monitoring
- Reproducibility
Disadvantages
- Can be complex to set up
- May not be suitable for small teams
- Can be expensive
Frequently Asked Questions
-
Q:Can I deploy Neptune on-premises or in a private cloud?
A:Yes, you can deploy Neptune on your on-prem infrastructure or in your private cloud. It is a set of microservices distributed as a Helm chart that you deploy on Kubernetes. -
Q:Can I version datasets without uploading them to Neptune?
A:Yes, you can just reference datasets that sit on your infrastructure or in the cloud. For example, you can have your datasets on S3 and just reference the bucket. -
Q:What is the benefit vs MLflow?
A:People choose Neptune when they don’t want to maintain infrastructure, they keep scaling their projects, or they collaborate with a team.
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