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Comet.ml

10
35
+ 1
3
MLflow

110
350
+ 1
6
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Comet.ml vs MLflow: 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, MLflow is detailed as "An open source machine learning platform". MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

Comet.ml and MLflow can be primarily classified as "Machine Learning" tools.

MLflow is an open source tool with 20 GitHub stars and 11 GitHub forks. Here's a link to MLflow's open source repository on GitHub.

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Pros of Comet.ml
Pros of MLflow
  • 3
    Best tool for comparing experiments
  • 3
    Code First
  • 3
    Simplified Logging

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- No public GitHub repository available -

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 MLflow?

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

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

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What are some alternatives to Comet.ml and MLflow?
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.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
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.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
CUDA
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
See all alternatives