AutoMLPipeline vs Datatron

AutoMLPipeline vs Datatron: What are the differences?

What is 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.

What is Datatron? Production AI Model Management at Scale. Automate the standardized deployment, monitoring, governance, and validation of all your models to be developed in any environment.

AutoMLPipeline and Datatron belong to "Machine Learning Tools" category of the tech stack.

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, Datatron provides the following key features:

• Explore models built and uploaded by your Data Science team, all from one centralized repository
• Create and scale model deployments in just a few clicks. Deploy models developed in any framework or language
• Make better business decisions to save your team time and money. Monitor model performance and detect model decay as it happens