Keras

715
690
+ 1
12
MLflow

73
233
+ 1
3
Add tool

Keras vs MLflow: What are the differences?

Developers describe Keras as "Deep Learning library for Theano and TensorFlow". Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/. 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.

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

Some of the features offered by Keras are:

  • neural networks API
  • Allows for easy and fast prototyping
  • Convolutional networks support

On the other hand, MLflow provides the following key features:

  • Track experiments to record and compare parameters and results
  • Package ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production
  • Manage and deploy models from a variety of ML libraries to a variety of model serving and inference platforms

Keras and MLflow are both open source tools. Keras with 42.5K GitHub stars and 16.2K forks on GitHub appears to be more popular than MLflow with 23 GitHub stars and 13 GitHub forks.

Advice on Keras and MLflow
Adithya Shetty
Student at PES UNIVERSITY · | 5 upvotes · 40.4K views
Needs advice
on
TensorFlow
PyTorch
and
Keras

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

See more
Pros of Keras
Pros of MLflow

Sign up to add or upvote prosMake informed product decisions

Cons of Keras
Cons of MLflow
    No cons available

    Sign up to add or upvote consMake informed product decisions

    What is Keras?

    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

    What is MLflow?

    MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
    What companies use Keras?
    What companies use MLflow?

    Sign up to get full access to all the companiesMake informed product decisions

    What tools integrate with Keras?
    What tools integrate with MLflow?

    Sign up to get full access to all the tool integrationsMake informed product decisions

    What are some alternatives to Keras and MLflow?
    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.
    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.
    MXNet
    A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
    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
    Interest over time