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  1. Stackups
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  4. Machine Learning Tools
  5. Comet.ml vs Neptune

Comet.ml vs Neptune

OverviewComparisonAlternatives

Overview

Comet.ml
Comet.ml
Stacks12
Followers50
Votes3
Neptune
Neptune
Stacks16
Followers38
Votes2

Comet.ml vs Neptune: What are the differences?

Key Differences Between Comet.ml and Neptune

Comet.ml and Neptune are both machine learning experiment management tools that provide features such as experiment tracking, collaboration, and visualization. However, they have several key differences that set them apart.

  1. Pricing Model: Comet.ml offers pricing based on a monthly subscription plan, starting from $19 per month for individuals, while Neptune follows a pay-per-usage model, with plans ranging from free for individual use and scaling up for teams and enterprise customers.

  2. Real-time Monitoring: Comet.ml allows real-time monitoring of experiments, enabling users to view live charts and metrics during experiment execution. In contrast, Neptune provides extensive offline exploration of experiment results, with visualizations and analysis available once the experiment has completed.

  3. Visualization Flexibility: Comet.ml focuses on providing a wide range of interactive and customizable visualizations, allowing users to explore and analyze their experiment results in various ways. Neptune, on the other hand, provides a more structured and standardized set of visualizations, ensuring consistency and easy comparison across experiments.

  4. Experiment Comparison: Comet.ml offers advanced options for comparing multiple experiments, including visual diffing, in order to analyze changes in model performance. Neptune also allows experiment comparison but primarily focuses on comparing experiment parameters and metrics, with less emphasis on visual comparison.

  5. Git Integration: Comet.ml integrates with Git, allowing users to automatically log code changes and track code versions alongside their experiment results. Neptune also offers Git integration but includes additional features like Jupyter Notebook integration and IDE integrations for popular development environments.

  6. Collaboration Features: Comet.ml provides collaboration features like shared workspaces and real-time experiment sharing, facilitating team collaboration and knowledge sharing. Neptune also supports collaboration, allowing team members to access and contribute to shared projects and experiments, as well as providing features for generating reports and sharing experiment results externally.

In summary, Comet.ml and Neptune differ in their pricing models, real-time monitoring capabilities, visualization flexibility, experiment comparison options, integrations (such as Git), and collaboration features.

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Detailed Comparison

Comet.ml
Comet.ml
Neptune
Neptune

Comet.ml allows data science teams and individuals to automagically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility.

It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools.

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Experiment tracking; Experiment versioning; Experiment comparison; Experiment monitoring; Experiment sharing; Notebook versioning
Statistics
Stacks
12
Stacks
16
Followers
50
Followers
38
Votes
3
Votes
2
Pros & Cons
Pros
  • 3
    Best tool for comparing experiments
Pros
  • 1
    Supports both gremlin and openCypher query languages
  • 1
    Aws managed services
Cons
  • 1
    Doesn't have much community support
  • 1
    Doesn't have proper clients for different lanuages
  • 1
    Doesn't have much support for openCypher clients
Integrations
TensorFlow
TensorFlow
Theano
Theano
scikit-learn
scikit-learn
PyTorch
PyTorch
Keras
Keras
PyTorch
PyTorch
Keras
Keras
R Language
R Language
MLflow
MLflow
Matplotlib
Matplotlib

What are some alternatives to Comet.ml, Neptune?

TensorFlow

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

scikit-learn

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

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