MLflow vs PyBrain

PyBrain vs MLflow: What are the differences?

Developers describe PyBrain as "A modular Machine Learning Library for Python". It's goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms. On the other hand, MLflow is detailed as "An open source machine learning platform". MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

PyBrain and MLflow belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by PyBrain are:

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

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

• 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

MLflow is an open source tool with 63 GitHub stars and 27 GitHub forks. Here's a link to MLflow's open source repository on GitHub.