Hummingbird vs MLflow

MLflow vs Hummingbird: 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; Hummingbird: Compile trained ML models into tensor computation (By Microsoft). It is a library for compiling trained traditional ML models into tensor computations. It allows users to seamlessly leverage neural network frameworks (such as PyTorch) to accelerate traditional ML models.

MLflow and Hummingbird 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, Hummingbird provides the following key features:

• Current and future optimizations implemented in neural network frameworks
• Native hardware acceleration
• Convert your trained traditional ML models into PyTorch

MLflow is an open source tool with 56 GitHub stars and 24 GitHub forks. Here's a link to MLflow's open source repository on GitHub.