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Compare Neptune vs MLflow

If you want to scale your model development,
you need Neptune.

neptune-logo
vs
mlflow

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.

5-min team workspace set-up
Flexible API for model tracking & packaging
Dedicated user support

Choose Neptune when bare-bones
metadata management is holding you back

icon 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.

quote
MLflow requires what I like to call software kung fu, because you need to host it yourself. So you have to manage the entire infrastructure — sometimes it’s good, oftentimes it’s not.
Senior Data Scientist Healthcare Analytics Platform, UK
#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")
icon 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.

quote
We tried MLflow. But the problem is that they have no user management features, which messes up a lot of things.
AI/ML Product Manager Customer Service Automation Platform, USA
icon 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.

quote
Neptune’s UI is highly configurable, which is way better than MLflow.
Chief Data Scientist HR Software Startup, Asia
icon 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.

quote
In MLflow, when I log a CSV file that’s about 10,000 rows, MLflow just stops working. I click on the CSV file, it may take maybe three minutes before it shows up, and even when it starts, it doesn’t work smoothly anymore. It’s totally unusable but that’s not a problem with Neptune.
Kha Nguyen Senior Data Scientist @ Zoined
Feature-by-feature comparison

Take a deep dive into
what makes Neptune different

Commercial Requirements

Commercial requirements chevron
Standalone component or a part of a broader ML platform?

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

Is the product available on-premises and / or in your private/public cloud?

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

Is the product delivered as commercial software, open-source software, or a managed cloud service?

Managed cloud service

Open-source

SLOs / SLAs: Does the vendor provide guarantees around service levels?
Support: Does the vendor provide 24×7 support?

No

SSO, ACL: does the vendor provide user access management?

No

Security policy and compliance

No

General Capabilities

Setup chevron
What are the infrastructure requirements?

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

How much do you have to change in your training process?

Minimal. Just a few lines of code needed for tracking. Read more

Minimal. Just a few lines of code needed for traking. Read more

Does it integrate with the training process via CLI/YAML/Client library?

Yes, through the neptune-clientlibrary

Does it come with a web UI or is it console-based?
Serverless UI

No

Yes

Flexibility, speed, and accessibility chevron
Customizable metadata structure

Yes

No

How can you access model metadata?
– gRPC API

No

No

– CLI / custom API

Yes

No

– REST API

No

Yes

– Python SDK

Yes

Yes

– R SDK

Yes

Yes

– Java SDK

No

Yes

– Julia SDK

No

No

Supported operations
– Search

Yes

Yes

– Update

Yes

– Delete

Yes

Yes

– Download

Yes

Yes

Distributed training support

Yes

Pipelining support

Yes

Yes

Logging modes
– Offline

Yes

Yes

– Debug

Yes

No

– Asynchronous

Yes

Yes

– Synchronous

Yes

Live monitoring

Yes

Yes

Mobile support

No

No

Webhooks and notifications

No

No

Experiment Tracking

Log and display of metadata chevron
Dataset
– location (path/s3)

Yes

Yes

– hash (md5)

Yes

Yes

– Preview table

Yes

No

– Preview image

Yes

Yes

– Preview text

Yes

Yes

– Preview rich media

No

– Multifile support

Yes

Yes

– Dataset slicing support

No

No

Code versions
– Source

Yes

Yes

– Notebooks

Yes

Parameters

Yes

Yes

Metrics and losses
– Single values

Yes

Yes

– Series values

Yes

Yes

– Series aggregates (min/max/avg/var/last)

Yes

Yes

Tags

Yes

Yes

Descriptions/comments

Yes

Yes

Rich format
– Images (support for labels and descriptions)

Yes

No

– Plots

Yes

Yes

– Interactive visualizations (widgets and plugins)

Yes

No

– Video

Yes

No

– Audio

Yes

No

– Neural Network Histograms

No

No

– Prediction visualization (tabular)

No

No

– Prediction visualization (image)

No

No

– Prediction visualization (image – interactive confusion matrix for image classification)

No

NA

– Prediction visualization (image – overlayed prediction masks for image segmentation)

No

NA

– Prediction visualization (image – overlayed prediction bounding boxes for object detection)

No

NA

Hardware consumption
– CPU

Yes

No

– GPU

Yes

No

– TPU

No

No

– Memory

Yes

No

System information
– Console logs (Stderr, Stdout)

Yes

No

– Error stack trace

Yes

No

– Execution command

No

Yes

– System details (host, user, hardware specs)

Yes

No

Environment config
– pip requirements.txt

Yes

Yes

– conda env.yml

Yes

Yes

– Docker Dockerfile

Yes

Yes

Files
– Model binaries

Yes

Yes

– CSV

Yes

Yes

– External file reference (s3 buckets)

Yes

Yes

Comparing experiments chevron
Table format diff

Yes

No

Overlayed learning curves

Yes

Yes

Parameters and metrics
– Groupby on experiment values (parameters)

Yes

– Parallel coordinates plots

Yes

No

– Parameter Importance plot

No

No

– Slice plot

No

No

– EDF plot

No

No

Rich format (side by side)
– Image

Yes

No

– Video

No

No

– Audio

No

No

– Plots

No

No

– Interactive visualization (HTML)

No

No

– Text

Yes

No

– Neural Network Histograms

No

No

– Prediction visualization (tabular)

Yes

Yes

– Prediction visualization (image, video, audio)

No

No

Code
– Git

No

No

– Source files

No

No

– Notebooks

Yes

No

Environment
– pip requirements.txt

No

No

– conda env.yml

No

No

– Docker Dockerfile

No

No

Hardware
– CPU

Yes

No

– GPU

Yes

No

– Memory

Yes

No

System information
– Console logs (Stderr, Stdout)

Yes

No

– Error stack trace

Yes

No

– Execution command

No

No

– System details (host, owner)

Yes

Yes

Data versions
– Location

Yes

No

– Hash

Yes

No

– Dataset diff

Yes

No

– External reference version diff (s3)

No

No

Files
– Models

No

No

– CSV

No

No

Custom compare dashboards
– Combining multiple metadata types (image, learning curve, hardware)

Yes

No

– Logging custom comparisons from notebooks/code

Yes

No

– Compare/diff of multiple (3+) experiments/runs

Yes

Yes

Organizing and searching experiments and metadata chevron
Experiment table customization
– Adding/removing columns

Yes

Yes

– Renaming columns in the UI

Yes

No

– Adding colors to columns

Yes

No

– Displaying aggregate (min/max/avg/var/last) for series like training metrics in a table

Yes

No

– Automagical column suggestion

Yes

No

Experiment filtering and searching
– Searching on multiple criteria

Yes

Yes

– Query language vs fixed selectors

Query language

Query language

– Saving filters and search history

Yes

No

Custom dashboards for a single experiment
– Can combine different metadata types in one view

Yes

No

– Saving experiment table views

Yes

No

– Logging project-level metadata

Yes

No

– Custom widgets and plugins

No

No

Tagging and searching on tags

Yes

Yes

Nested metadata structure support in the UI

Yes

No

Reproducibility and traceability chevron
One-command experiment re-run

No

Yes

Experiment lineage
– List of datasets used downstream

No

No

– List of other artifacts (models) used downstream

No

No

– Downstream artifact dependency graph

No

No

Reproducibility protocol

Limited

Yes

Is environment versioned and reproducible

Yes

Yes

Saving/fetching/caching datasets for experiments

No

No

Collaboration and knowledge sharing chevron
Sharing UI links with project members

Yes

No

Sharing UI links with external people

Yes

No

Commenting

Yes

Yes

Interactive project-level reports

No

No

Model Registry

Model versioning chevron
Code versions (used for training)

Yes

Environment versions

No

Yes

Parameters

Yes

Yes

Dataset versions

Yes

No

Results (metrics, visualizations)

Yes

Yes

Explanations (SHAP, DALEX)

Yes

Model files (packaged models, model weights, pointers to artifact storage)

Yes

Yes

Model lineage and evaluation history chevron
Models/experiments created downstream

No

No

History of evaluation/testing runs

No

No

Support for continuous testing

No

No

Users who created a model or downstream experiments

No

No

Access control, model review, and promoting models chevron
Main stage transition tags (develop, stage, production)

Yes

Yes

Custom stage tags

No

No

Locking model version and downstream runs, experiments, and artifacts

No

No

Adding annotations/comments and approvals from the UI

No

Model compare (current vs challenger etc)

Yes

Compatibility audit (input/output schema)

No

Yes

Compliance audit (datasets used, creation process approvals, results/explanations approvals)

No

No

CI/CD/CT compatibility chevron
Webhooks

No

No

Model accessibility

No

Yes

Support for continuous testing

No

No

Integrations with CI/CD tools

No

No

Model searching chevron
Registered models

No

Yes

Active models

No

No

By metadata/artifacts used to create it

No

No

By date

No

No

By user/owner

No

No

By production stage

No

No

Search query language

No

No

Model packaging chevron
Native packaging system

No

Yes

Compatibility with packaging protocols (ONNX, etc)

No

Yes

One model one file or flexible structure

No

Integrations with packaging frameworks

No

Yes

Integrations and Support

Languages chevron
Java

No

Yes

Julia

No

No

Python

Yes

Yes

REST API

No

Yes

Model training chevron
Catalyst

Yes

Yes

CatBoost

No

Yes

fastai

Yes

Yes

FBProphet

Yes

Yes

Gluon

No

Yes

HuggingFace

Yes

Yes

H2O

No

Yes

LightGBM

Yes

Yes

Paddle

No

Yes

PyTorch

Yes

Yes

PyTorch Ignite

Yes

PyTorch Lightning

Yes

Yes

Scikit Learn

Yes

Yes

Skorch

Yes

Yes

Spacy

No

Yes

Spark MLlib

No

Yes

Statsmodel

No

Yes

TesorFlow / Keras

Yes

Yes

XGBoost

Yes

Yes

Hyperparameter Optimization chevron
Hyperopt

No

No

Keras Tuner

No

Optuna

Yes

Yes

Ray Tune

No

Yes

Scikit-Optimize

No

Model visualization and debugging chevron
DALEX

No

Netron

No

No

SHAP

No

Yes

TensorBoard
IDEs and Notebooks chevron
JupyterLab and Jupyter Notebook

Yes

Google Colab

Yes

Deepnote

Yes

AWS SageMaker

Yes

No

Data versioning chevron
DVC

Yes

No

Orchestration and pipelining chevron
Airflow

No

No

Argo

No

No

Kedro

Yes

Yes

Kubeflow

No

No

ZenML

Yes

Yes

Experiment tracking tools chevron
MLflow

NA

Sacred

Yes

No

TensorBoard

No

CI/CD chevron
GitHub Actions

Yes

Gitlab CI

No

CircleCI

No

Yes

Travis

No

Yes

Jenkins

No

No

Model serving chevron
Seldon

No

No

Cortex

No

No

Databricks

No

Yes

Model versioning chevron
Seldon

No

Fiddler.ai

No

No

Arthur.ai

No

No

This table was updated on 13 January 2023. Some information may be outdated.
Report outdated information here.
avatar lazyload
quote
For now, I’m not using ML flow anymore ever since I switched to Neptune because I feel like Neptune is a super-set of what MLflow has to offer.
Kha Nguyen Senior Data Scientist @Zoined

Make it simple to scale your model development

Neptune is the lightweight solution for ML teams growing frustrated with MLflow’s limited functionality.

Check out the best-fit plan for your business today.

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