Keras vs Continuous Machine Learning: 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, Continuous Machine Learning is detailed as "CI/CD for Machine Learning Projects". Continuous Machine Learning (CML) is an open-source library for implementing continuous integration & delivery (CI/CD) in machine learning projects. Use it to automate parts of your development workflow, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets.
Keras and Continuous Machine Learning 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, Continuous Machine Learning provides the following key features:
- GitFlow for data science
- Auto reports for ML experiments
- No additional services
Keras is an open source tool with 48.9K GitHub stars and 18.4K GitHub forks. Here's a link to Keras's open source repository on GitHub.
