Neptune vs MLflow
Which tool is better?

Zero maintenance

Is it easy to set up and maintain MLflow server and visualization dashboard for your entire team?

With Neptune you get all your experiment data saved and backed-up on a hosted server or on-prem installation. You can manage user permissions and share experiments in the beautiful UI with no additional overhead. Powerful, simple, and available for you and your team in minutes.

User Management

Can you manage user permissions in MLflow?

Neptune lets you manage users easily. You can give people access to projects and organizations. Additionally, Neptune gives you control over the user role: admin, contributor or viewer.

Fast UI

Is your MLflow UI loading slowly when you have thousands of runs?

Neptune was built to scale and can support millions of experiment runs both in the back-end and front-end.

Dashboard Views

Can you save different experiment dashboard views in MLflow?

When you have multiple users working on many ideas the things you want to look at change… and so should your experiment dashboard.
Neptune lets you customize, change, and save experiment dashboard views depending on your needs.

Resource Monitoring

Can you monitor your hardware in MLflow?

Neptune lets you monitor your resource consumption of your CPU, GPU, and Memory.
With that, you can optimize your code to utilize your resources fully.

Image Channel Display

Can you scroll through your images and charts?

Neptune lets you log images and charts to multiple image channels and scroll through them to quickly see the progress of your model training. Get a full picture of what is happening in your training and validation loops by leveraging more information!

Notebook Autosnapshots

Does MLflow snapshot your Jupyter notebooks automatically?

Neptune notebook integration automatically snapshots your .iipynb whenever you run a cell with neptune.create_experiment() in it. Whether you remember to submit your experiment or not everything will be safely versioned and ready to be explored.

Analyze Experiment Dashboard in Jupyter Notebook

Does MLflow let you fetch your experiment dashboard directly to a pandas DataFrame?

With Neptune you can fetch whatever information you or your teammates tracked and explore it however you like. We have some nice exploratory features, like HiPlot integration to help you with that.

neptune.init('USERNAME/example-project')

make_parallel_coordinates_plot(

     metrics= ['eval_accuracy', 'eval_loss',...],

     params = ['activation', 'batch_size',...])

Notebook Versioning and Diffing

Does MLflow let you track your exploratory analysis?

Neptune goes beyond the tracking of machine learning experiments and allows you to version your exploratory data analysis or results exploration as well!
Once it is saved in Neptune you can name, share, download or see diffs of your notebook checkpoints.

MLflow integration

Can you use MLflow Interface with Neptune back-end and UI?

That’s right. Neptune integrates with MLflow to give you the best of both worlds.
You can use MLflow interface for experiment tracking, sync your mlruns folder with Neptune and enjoy the awesome UI that Neptune gives you. No need to keep your mlruns folder backed-up, or firing mlflow UI dashboard on a dedicated server to explore it. Just sync mlruns with Neptune:

neptune mlflow PATH/TO/mlruns \

       --project USER_NAME/PROJECT_NAME