AutoMLPipeline vs Tensorflow Lite

AutoMLPipeline vs Tensorflow Lite: 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, Tensorflow Lite is detailed as "Deploy machine learning models on mobile and IoT devices". It is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and a small binary size.

AutoMLPipeline and Tensorflow Lite 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, Tensorflow Lite provides the following key features:

• Lightweight solution for mobile and embedded devices
• Enables low-latency inference of on-device machine learning models with a small binary size
• Fast performance

AutoMLPipeline is an open source tool with 164 GitHub stars and 9 GitHub forks. Here's a link to AutoMLPipeline's open source repository on GitHub.