Trax vs AutoMLPipeline: What are the differences?

Trax: Your path to advanced deep learning (By Google). It helps you understand and explore advanced deep learning. It is actively used and maintained in the Google Brain team You can use It either as a library from your own python scripts and notebooks or as a binary from the shell, which can be more convenient for training large models. It includes a number of deep learning models (ResNet, Transformer, RNNs, ...) and has bindings to a large number of deep learning datasets, including Tensor2Tensor and TensorFlow datasets. It runs without any changes on CPUs, GPUs and TPUs.; 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.

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

Some of the features offered by Trax are:

• Advanced deep learning
• Actively used and maintained in the Google Brain team
• Runs without any changes on CPUs, GPUs and TPUs

On the other hand, AutoMLPipeline provides the following key features:

• 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