Aerosolve vs Aquarium: What are the differences?

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; Aquarium: *Improve Your ML Dataset Quality *. Machine learning models are only as good as the datasets they're trained on It helps ML teams make better models by improving their dataset quality..

Aerosolve and Aquarium 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, Aquarium provides the following key features:

• Upload your dataset to get a health check of its quality, quantity, and diversity. Zoom in and out of your dataset. Uncover distribution biases before you train. Find and fix labeling errors quickly
• Upload model inferences against your labeled datasets and deep dive into its performance. Find where your model is performing well and badly so you can take the best actions to improve it
• With knowledge of your dataset diversity and model performance, it automatically samples the best data to sample to label and retrain on. Your model performance just gets better

Aerosolve is an open source tool with 4.65K GitHub stars and 581 GitHub forks. Here's a link to Aerosolve's open source repository on GitHub.