Continuous Machine Learning vs MXNet

MXNet vs Continuous Machine Learning: What are the differences?

What is MXNet? A flexible and efficient library for deep learning. 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.

What is Continuous Machine Learning? 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.

MXNet and Continuous Machine Learning belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by MXNet are:

• Lightweight
• Portable
• Flexible distributed/Mobile deep learning

On the other hand, Continuous Machine Learning provides the following key features:

• GitFlow for data science
• Auto reports for ML experiments
• No additional services

MXNet is an open source tool with 18.9K GitHub stars and 6.69K GitHub forks. Here's a link to MXNet's open source repository on GitHub.