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.
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!!