Aerosolve vs DMTK: 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 DMTK? Microsoft Distributed Machine Learning Tookit. DMTK provides a parameter server based framework for training machine learning models on big data with numbers of machines. It is currently a standard C++ library and provides a series of friendly programming interfaces.
Aerosolve and DMTK can be categorized 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, DMTK provides the following key features:
- DMTK Framework: a flexible framework that supports unified interface for data parallelization, hybrid data structure for big model storage, model scheduling for big model training, and automatic pipelining for high training efficiency.
- LightLDA, an extremely fast and scalable topic model algorithm, with a O(1) Gibbs sampler and an efficient distributed implementation.
- Distributed (Multisense) Word Embedding, a distributed version of (multi-sense) word embedding algorithm.
Aerosolve and DMTK are both open source tools. It seems that Aerosolve with 4.58K GitHub stars and 578 forks on GitHub has more adoption than DMTK with 2.69K GitHub stars and 595 GitHub forks.