Continuous Machine Learning vs DMTK

DMTK vs Continuous Machine Learning: What are the differences?

Developers describe DMTK as "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. On the other hand, Continuous Machine Learning is detailed as "CI/CD for Machine Learning Projects". Continuous Machine Learning (CML) is an open-source library for implementing continuous integration & delivery (CI/CD) in machine learning projects. Use it to automate parts of your development workflow, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets.

DMTK and Continuous Machine Learning 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, Continuous Machine Learning provides the following key features:

• GitFlow for data science
• Auto reports for ML experiments
• No additional services

DMTK is an open source tool with 2.76K GitHub stars and 597 GitHub forks. Here's a link to DMTK's open source repository on GitHub.