Neptune vs Azure ML


Neptune

Azure ML
Commercial Requirements
Standalone component. ML metadata store that focuses on experiment tracking and model registry

Part of the Microsoft Azure ecosystem

Azure ML is available only as a fully managed cloud service.
Managed cloud service

Managed Cloud Service
General Capabilities
No special requirements other than having the neptune-client installed and access to the internet if using managed hosting. Check here for infrastructure requirements for on-prem deployment

Only internet access needed to access the Azure ecosystem
Yes, through the neptune-client library

Yes, through the Azure ML CLI and SDKs
No

No

No

No


No

No
Experiment Tracking

No

No



No

No
No

No
No

No
No

No

No

No

No

No

No



No

No
No

No
No

No

No

No
No

No
No

No
No

No
No

No
No

No

No

No
No

No

No

No
No

No
No

No


No

No

No

No

No

No
No

No

No

No

Yes

No
Yes

No

Query language

Fixed selectors

No

No
No

No

No

No
No

No
No

No
Limited

No
Model Registry
No

No
No

No
Limited

No
No

No
No

No
Integrations and Support
No

No
No

No
No

No
No

No
No

No
No

No
What are the key advantages of Neptune then?

- Focus on the experiment tracking and model registry
- More extensive comparison functionality
- Customizable metadata structure
- Team collaboration features
See these features in action
Sign up to Neptune and install client library
pip install neptune
Track experiments
import neptune
run = neptune.init_run()
run["params"] = {
"lr": 0.1, "dropout": 0.4
}
run["test_accuracy"] = 0.84
Register models
import neptune
model = neptune.init_model()
model["model"] = {
"size_limit": 50.0,
"size_units": "MB",
}
model["model/signature"].upload(
"model_signature.json")
Thousands of ML people already chose their tool
It only takes 5 minutes to integrate Neptune with your code
Don’t overthink it