Deepkit vs MLflow

MLflow vs Deepkit: What are the differences?

MLflow: An open source machine learning platform. MLflow is an open source platform for managing the end-to-end machine learning lifecycle; Deepkit: The collaborative and analytical AI training suite. It is the collaborative and analytical training suite for insightful, fast, and reproducible modern machine learning. All in one cross-platform desktop app for you alone, corporate or open-source teams.

MLflow and Deepkit 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, Deepkit provides the following key features:

• Real-time UI and collaboration
• Unified experiments
• Model debugger

MLflow is an open source tool with 52 GitHub stars and 22 GitHub forks. Here's a link to MLflow's open source repository on GitHub.