If you want to scale your model development,
you need Neptune.
MLflow is great for Data Scientists and ML engineers looking for a basic ML lifecycle platform.
But it doesn’t give you the functionality or collaborative features you need
as your team and metadata management grow in size. Neptune does.
Choose Neptune when bare-bones
metadata management is holding you back
Maintenance
SaaS = Zero maintenance
It’s frustrating to spend your days dealing with storage & backups, managing user access, and setting up autoscaling for your servers. Not to mention the need to create new MLflow instances for every project.
Neptune’s SaaS solution lets you work on multiple projects & handles your backend automatically. So you can focus on managing your model development.
#Sign up to Neptune and install client library
pip install neptune-client
#Track experiments
import neptune.new as neptune
run = neptune.init_run()
run["params"] = {"lr": 0.1, "dropout": 0.4}
run["test_accuracy"] = 0.84
#Register models
model = neptune.init_model()
model["model"] = {
"size_limit": 50.0, "size_units": "MB"}
model["model/signature"].upload(
"model_signature.json")
Team
Created for collaboration
MLflow is great for individuals. But the limitations of open-source software for access management and experiment sharing start to bite as soon as your team expands.
Packed with collaborative features — like customizable workspaces and persistent shareable links — Neptune takes team management off your to-do list.
Interface
Debug your models faster with a flexible User Interface
MLflow’s limited visualization capabilities make it hard to explore experiments.
With Neptune, you can compare all of your metadata in a clean, easy-to-navigate, and responsive User Interface. With searchable side-by-side run tables, parallel coordinates plots, and learning curve charts, Neptune makes it easy to analyze experiments.
Scalability
Will scale. Won’t fail.
Unlike MLflow, Neptune won’t freeze up faced with large streams of logs running 1000s of experiments at once. And even when rendering complex charts to view your data ― like Matpolib figures or Bokeh plots ― Neptune will never slow you down.
Take a deep dive into
what makes Neptune different
Neptune
MLflow
Commercial Requirements
Standalone component. ML metadata store that focuses on experiment tracking and model registry
Open-source platform which offers four separate components for experiment tracking, code packaging, model deployment, and model registry
Tracking is hosted on a local/remote server (on-prem or cloud). Is also available on a managed server as part of the Databricks platform
Managed cloud service
Open-source
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
No requirements other than having mlflow installed if using a local tracking server. Check here for infrastructure requirements for using a remote tracking server
Yes, through the neptune-clientlibrary
Yes, they have SDK clients in various languages, and a CLI
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Experiment Tracking
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Query language
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Model Registry
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Integrations and Support
Report outdated information here.
Make it simple to scale your model development
Neptune is the lightweight solution for ML teams growing frustrated with MLflow’s limited functionality.