ScalaNLP vs Continuous Machine Learning: What are the differences?

ScalaNLP: A suite of machine learning and numerical computing libraries. ScalaNLP is a suite of machine learning and numerical computing libraries; 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.

ScalaNLP and Continuous Machine Learning can be primarily classified as "Machine Learning" tools.

Some of the features offered by ScalaNLP are:

• ScalaNLP is the umbrella project for several libraries:
• Breeze is a set of libraries for machine learning and numerical computing
• Epic is a high-performance statistical parser and structured prediction library

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

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

ScalaNLP is an open source tool with 3.14K GitHub stars and 685 GitHub forks. Here's a link to ScalaNLP's open source repository on GitHub.