DMTK vs Xcessiv: What are the differences?
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.
What is Xcessiv? Fully managed web application for automated machine learning. A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
DMTK and Xcessiv can be primarily classified as "Machine Learning" tools.
Some of the features offered by DMTK are:
- 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.
On the other hand, Xcessiv provides the following key features:
- Fully define your data source, cross-validation process, relevant metrics, and base learners with Python code
- Any model following the Scikit-learn API can be used as a base learner
- Task queue based architecture lets you take full advantage of multiple cores and embarrassingly parallel hyperparameter searches
DMTK and Xcessiv are both open source tools. DMTK with 2.69K GitHub stars and 595 forks on GitHub appears to be more popular than Xcessiv with 1.19K GitHub stars and 95 GitHub forks.