AutoMLPipeline vs Neptune: What are the differences?

Developers describe AutoMLPipeline as "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. On the other hand, Neptune is detailed as "The most lightweight experiment tracking tool for machine learning". It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools.

AutoMLPipeline and Neptune can be categorized 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, Neptune provides the following key features:

• Experiment tracking
• Experiment versioning
• Experiment comparison