Aerosolve vs Paperspace: 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, Paperspace is detailed as "The way to access and manage limitless computing power in the cloud". It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines.
Aerosolve and Paperspace 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, Paperspace provides the following key features:
- Intelligent alert
- Two-factor authentication
- Share drives
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.