Aerosolve vs Hummingbird: What are the differences?

What is Aerosolve? A machine learning package built for humans (created by Airbnb). This library is meant to be used with sparse, interpretable features such as those that commonly occur in search (search keywords, filters) or pricing (number of rooms, location, price). It is not as interpretable with problems with very dense non-human interpretable features such as raw pixels or audio samples.

What is 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.

Aerosolve and Hummingbird belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by Aerosolve are:

• A thrift based feature representation that enables pairwise ranking loss and single context multiple item representation.
• A feature transform language gives the user a lot of control over the features
• Human friendly debuggable models

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

Aerosolve is an open source tool with 4.65K GitHub stars and 580 GitHub forks. Here's a link to Aerosolve's open source repository on GitHub.