GraphPipe vs MLflow: What are the differences?
What is GraphPipe? Machine Learning Model Deployment Made Simple, by Oracle. GraphPipe is a protocol and collection of software designed to simplify machine learning model deployment and decouple it from framework-specific model implementations.
What is MLflow? An open source machine learning platform. MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
GraphPipe and MLflow belong to "Machine Learning Tools" category of the tech stack.
Some of the features offered by GraphPipe are:
- A minimalist machine learning transport specification based on flatbuffers
- Simple, efficient reference model servers for Tensorflow, Caffe2, and ONNX.
- Efficient client implementations in Go, Python, and Java.
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
GraphPipe and MLflow are both open source tools. It seems that GraphPipe with 643 GitHub stars and 91 forks on GitHub has more adoption than MLflow with 23 GitHub stars and 13 GitHub forks.