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Aerosolve vs Google AutoML Tables: What are the differences?
Developers describe Aerosolve as "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. On the other hand, Google AutoML Tables is detailed as "Automatically build and deploy machine learning models on structured data". Enables your entire team of data scientists, analysts, and developers to automatically build and deploy machine learning models on structured data at massively increased speed and scale.
Aerosolve and Google AutoML Tables can be primarily classified as "Machine Learning" tools.
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, Google AutoML Tables provides the following key features:
- Increases model quality
- Easy to build models
- Easy to deploy
Aerosolve is an open source tool with 4.58K GitHub stars and 578 GitHub forks. Here's a link to Aerosolve's open source repository on GitHub.