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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.
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- Simplified Logging4
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What is Lobe?
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.
What is MLflow?
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
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