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MLflow
MLflow

11
20
+ 1
0
TensorFlow.js
TensorFlow.js

57
119
+ 1
6
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MLflow vs TensorFlow.js: What are the differences?

Developers describe MLflow as "An open source machine learning platform". MLflow is an open source platform for managing the end-to-end machine learning lifecycle. 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.

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

MLflow and TensorFlow.js are both open source tools. TensorFlow.js with 11.1K GitHub stars and 801 forks on GitHub appears to be more popular than MLflow with 20 GitHub stars and 11 GitHub forks.

What is MLflow?

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

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
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        What tools integrate with MLflow?
        What tools integrate with TensorFlow.js?
        What are some alternatives to MLflow and TensorFlow.js?
        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.
        Airflow
        Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
        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/
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