Stacks5

Followers26

+ 1

Votes1

TensorFlow.js

Stacks100

Followers209

+ 1

Votes9

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Comet.ml vs TensorFlow.js: 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, TensorFlow.js is detailed as "Machine Learning in JavaScript". 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.

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

TensorFlow.js is an open source tool with 11.2K GitHub stars and 816 GitHub forks. Here's a link to TensorFlow.js's open source repository on GitHub.

Pros of Comet.ml

Pros of TensorFlow.js

1
Best tool for comparing experiments

4
NodeJS Powered
4
Open Source
1
Deploy python ML model directly into javascript

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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 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

What companies use Comet.ml?

What companies use TensorFlow.js?

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What tools integrate with Comet.ml?

What tools integrate with TensorFlow.js?

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What are some alternatives to Comet.ml and TensorFlow.js?

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.

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.

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.

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Comet.ml vs TensorFlow.js