AutoMLPipeline vs Continuous Machine Learning

AutoMLPipeline vs Continuous Machine Learning: What are the differences?

AutoMLPipeline: A package that makes it trivial to create and evaluate machine learning pipeline architectures (by IBM). It is a package that makes it trivial to create complex ML pipeline structures using simple expressions. It leverages on the built-in macro programming features of Julia to symbolically process, manipulate pipeline expressions, and automatically discover optimal structures for machine learning prediction and classification; 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.

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

Some of the features offered by AutoMLPipeline are:

• Pipeline API that allows high-level description of processing workflow
• Common API wrappers for ML libs including Scikitlearn, DecisionTree, etc
• Symbolic pipeline parsing for easy expression of complexed pipeline structures

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

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

AutoMLPipeline is an open source tool with 185 GitHub stars and 18 GitHub forks. Here's a link to AutoMLPipeline's open source repository on GitHub.