Yottamine

Yottamine is a Big Solution to meet Big Data requirements. Yottamine has been developed as an advanced machine learning solution for data modeling and can scale to easily manage huge data volumes. In order to assure the most efficient processing of these (and smaller) data sets, special emphasis has been placed maintaining best practices in high-performance computing.

Yottamine draws from supercomputing to utilize MPI parallel processing, which enables superior performance in scalable large-scale parallel applications.

MPI is the preferred approach for managing resources for both in memory processing as well as managing resources across high performance clusters. Yottamine’s distributed memory approach is superior to disk based model computations and enables Yottamine to deliver the fastest possible computing on large data sets.

Yottamine’s analytic solutions leverage Hadoop’s scalability, fault tolerance, and ability to manage structured and unstructured data. Using Hadoop’s distributed file system and Amazon’s Elastic Cloud Computer for processing, Yottamine’s innovative design combines MPI’s capabilities with the best features of the Hadoop framework. Yottamine’s predictive models can handle terabyte sized data training sets and efficiently apply the models in the largest data stores and operational settings.

Yottamine’s simple and versatile API allows integration with multiple data sources and applications. Yottamine is implemented as an easy to use web service, it can be run locally or in the cloud and can integrate with a wide variety of sources and tools. Yottamine is hosted by Amazon Web Services (AWS).

AWS data centers are located in the U.S., Europe, Singapore, and Japan, and can launch hundreds or thousands of servers in minutes to meet customer performance requirements. AWS is a secure and durable technology platform with industry recognized certifications and audits.

YottamineOP is based on advanced machine learning theory that can take all available features into consideration without sacrificing model accuracy. It reduces the amount of iteration required for the data scientist in figuring out which variables are important. In other words, it takes full advantage of the depth and volume of big data stored in your Enterprise Data Hub.