Lobe vs MLflow: What are the differences?
Lobe: Deep learning made simple. An easy-to-use visual tool that lets you build custom deep learning models, quickly train them, and ship them directly in your app without writing any code; MLflow: An open source machine learning platform. MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
Lobe and MLflow can be primarily classified as "Machine Learning" tools.
Some of the features offered by Lobe are:
- Build - Drag in your training data and Lobe automatically builds you a custom deep learning model. Then refine your model by adjusting settings and connecting pre-trained building blocks.
- Train - Monitor training progress in real-time with interactive charts and test results that update live as your model improves. Cloud training lets you get results quickly, without slowing down your computer.
- Ship - Export your trained model to TensorFlow or CoreML and run it directly in your app on iOS and Android. Or use the easy-to-use Lobe Developer API and run your model remotely over the air.
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 20 GitHub stars and 11 GitHub forks. Here's a link to MLflow's open source repository on GitHub.