Molecula is a data virtualization company than enables instantaneous, secure access to large, fragmented and geographically dispersed datasets to support demanding Machine Learning (ML) and Artificial Intelligence (AI) workloads. Molecula’s technology provides real-time virtualized access to all data in-memory. This avoids data movement and provides a layer of abstraction above physical implementation of data, irrespective of the source, how it is formatted and where it is physically located. Molecula’s mission is to reliably leverage machine-led and machine-assisted decisions to unlock human potential. Molecula is tackling AI data readiness, which is said to be the main problem preventing large scale AI adoption in the enterprise as of 2019.
The Molecula core is built on the open-source project Pilosa, an open-source bitmap index, which basically decouples data storage or multiple copies of data to present in scalable form.
Pilosa has more than 1650 enterprise and academic organizations as its users and contributors from May 2017 to May 2019. Higinio (H.O.) Maycotte, CEO, says that Pilosa was built because there was not any technology to solve the long data access cycles to do analytics and ML. Enterprise grade capabilities like data management, data pooling, machine learning add-ons, monitoring and executive reporting will be available with Molecula’s platform.
The cloud environment has led to the creation of multiple copies and storages of data. Molecula can help large organizations move their data workloads to the cloud without having copies and have customers pay per VDS (virtual dedicated server) deployed rather than for the storage. The idea is to allow data to be compressed and available to data engineers faster.
As of September 2019 Molecula had 10 paying customers and works with Oracle to take its product to market. Paying customers include media houses and Fortune 500 companies.
Molecula raises a $6,000,000 seed round from Lontra Ventures, Velar Capital, Seraph Group and Capital Factory.
Molecula was founded.
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