MLflow vs Tensor2Tensor

MLflow vs Tensor2Tensor: What are the differences?

Developers describe MLflow as "An open source machine learning platform". MLflow is an open source platform for managing the end-to-end machine learning lifecycle. On the other hand, Tensor2Tensor is detailed as "Library of deep learning models & datasets designed to make deep learning more accessible (by Google Brain)". It is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. It was developed by researchers and engineers in the Google Brain team and a community of users.

MLflow and Tensor2Tensor can be primarily classified as "Machine Learning" tools.

Some of the features offered by MLflow are:

• Track experiments to record and compare parameters and results
• Package ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production
• Manage and deploy models from a variety of ML libraries to a variety of model serving and inference platforms

On the other hand, Tensor2Tensor provides the following key features:

• Many state of the art and baseline models are built-in and new models can be added easily
• Many datasets across modalities - text, audio, image - available for generation and use, and new ones can be added easily
• Models can be used with any dataset and input mode (or even multiple)

MLflow and Tensor2Tensor are both open source tools. Tensor2Tensor with 9.7K GitHub stars and 2.51K forks on GitHub appears to be more popular than MLflow with 52 GitHub stars and 22 GitHub forks.