Trax vs Datatron: What are the differences?

What is 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..

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.

Trax and Datatron can be categorized as "Machine Learning" tools.

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, 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