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Comet.ml
Comet.ml

5
17
+ 1
1
MLflow
MLflow

12
23
+ 1
0
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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.

- 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 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.
        scikit-learn
        scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
        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.
        ML Kit
        ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
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