Research firm Gartner has identified five "cool vendors" in the data science and machine learning space, identifying the features that make their products especially unique or useful. The report, "5 Cool Vendors in Data Science and Machine Learning" was written by analysts Peter Krensky, Svetlana Sicular, Jim Hare, Erick Brethenoux and Austin Kronz. Here are the highlights of what they had to say about each vendor.

DimensionalMechanics

Bellevue, Washington
www.dimensionalmechanics.com
“DimensionalMechanics has built a data science platform that breaks from market traditions; where more conventional vendors have developed work flow-based or notebook-based data science environments, DimensionalMechanics has opted for a “data-science metalanguage,” Erick Brethenoux writes. “In effect, given the existing use cases the company has handled so far, its NeoPulse Framework 2.0 acts as an “AutoDL” (Auto-Deep Learning) platform. This makes new algorithms and approaches to unusual types of data (such as images, videos and sounds) more accessible and deployable.”

Immuta

College Park, Maryland
www.immuta.com
“Immuta offers a dedicated data access and management platform for the development of machine learning and other advanced analytics, and the automation of policy enforcement,” Peter Krensky and Jim Hare write. “The product serves as a control layer to rapidly connect and control access between myriad data sources and the heterogeneous array of data science tools without the need to move or copy data. This approach addresses the market expectation that platforms supporting data science will be highly flexible and extensible to the data portfolio and toolkit of a user’s choosing.”

Indico

Boston, Massachusetts
www.indico.io
“Indico offers a group of products with a highly accessible set of functionality for exploring and modeling unstructured data and automating processes,” according to Peter Krensky and Austin Kronz. “The offering can be described as a citizen data science toolkit for applying deep learning to text, images and document-based data. Indico’s approach makes deep learning a practical solution for subject matter experts (SMEs) facing unstructured content challenges. This is ambitious and exciting, as both deep learning and unstructured content analytics are areas where even expert data scientists are still climbing the learning curve.”

Octopai

Rosh HaAyin, Israel & New York, New York
www.octopai.com
“Octopai solves a foundational problem for data-driven organizations — enabling data science teams and citizen data scientists to quickly find the data, establish trust in data sources and achieve transparency of data lineage through automation,” explains Svetlana Sicular. “It connects the dots of complex data pipelines by using machine learning and pattern analysis to determine the relationships among different data elements, the context in which the data was created, and the data’s prior uses and transformations. Such access to more diverse, transparent and trustworthy data leads to better quality analytics and machine learning.”

ParallelM

Tel Aviv, Israel & Sunnyvale, California
www.parallelm.com
“ParallelM is one of the first software platforms principally focused on the data science operationalization process,” Erick Brethenoux writes. “The focus of data science teams has traditionally been on developing analytical assets, while dealing with the operationalization of these assets has been an afterthought. Deploying analytical assets within operational processes in a repeatable, manageable, secure and traceable manner requires more than a set of APIs and a cloud service; a model that has been scored (executed) has not necessarily been managed. ParallelM’s success and the general development of operationalization functionality within platforms will be an indicator of the success of an entire generation of data scientists.”

 Source: Information Management