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Comet.ml

10
35
+ 1
3
Kubeflow

141
459
+ 1
16
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Comet.ml vs Kubeflow: What are the differences?

Developers describe Comet.ml as "Track, compare and collaborate on Machine Learning experiments". 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. On the other hand, Kubeflow is detailed as "Machine Learning Toolkit for Kubernetes". 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.

Comet.ml and Kubeflow can be categorized as "Machine Learning" tools.

Kubeflow is an open source tool with 6.93K GitHub stars and 1K GitHub forks. Here's a link to Kubeflow's open source repository on GitHub.

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Pros of Comet.ml
Pros of Kubeflow
  • 3
    Best tool for comparing experiments
  • 8
    System designer
  • 3
    Customisation
  • 3
    Kfp dsl
  • 2
    Google backed

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What is Comet.ml?

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.

What is 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.

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What companies use Comet.ml?
What companies use Kubeflow?
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What tools integrate with Comet.ml?
What tools integrate with Kubeflow?

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What are some alternatives to Comet.ml and Kubeflow?
MLflow
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
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.
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
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
See all alternatives