Hudson Data Announces Launch Of Model Factory

Hudson Data, the Applied Data Science company, today announced Model Factory, a custom Machine Learning model building service designed to address a company's most urgent data science needs. Hudson Data has harnessed its many years of model building expertise, along with its many time-tested rules engines, to create a service that develops and deploys advanced ensemble ML models quickly. "Eliminating time-consuming, unproductive model guesswork is the key benefit of Model Factory," said Hudson Data CEO, Menish Gupta. "We can deploy custom, auditable, ML models within 60 days from inception to production." Companies looking to extract more value from their data are typically faced with added infrastructure and personnel costs. Model Factory can reduce or eliminate expensive staff training and IT investments by developing, configuring, and installing sophisticated models quickly, on prem or in the cloud. "We are confident our team can efficiently deliver impactful results, and with less risk," stated Rick Arturo, VP of Business Development. "In fact, if the Hudson Data model fails to achieve the targeted results, there is no cost for the model. We know of no other company offering such a pledge." Model Factory produces documented model artifacts and results which are ideal for regulatory compliance applications. Hudson Data solves some of the most challenging data science problems through the application of Machine Learning and highly advanced rules engines that infuse predictive analytics into applications. The company provides Graph analytics software and offers a Machine Learning-as-a-Service platform that easily integrates into existing infrastructures. Our award-winning consulting service deploys "Data Science Pods," that include a team of Delivery Managers, Data Scientists, Analysts, and Engineers, that design, build, and implement software analytics solutions. Hudson Data focuses on the Financial Services, Insurance, Healthcare, Government, and Logistics sectors.