Aerosolve vs Clipper: 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 Clipper? A prediction serving system for TensorFlow, PyTorch, PySpark and others. It is a low-latency prediction serving system for machine learning. Clipper makes it simple to integrate machine learning into user-facing serving systems.

Aerosolve and Clipper 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, Clipper provides the following key features:

• Simplifies integration of machine learning techniques
• Simplifies model deployment and helps reduce common bugs
• Improves throughput and ensures reliable millisecond latencies

Aerosolve is an open source tool with 4.6K GitHub stars and 578 GitHub forks. Here's a link to Aerosolve's open source repository on GitHub.