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Data Science Challenges in Production Environment 

A very little time is spent on thinking about how to deploy a data science model into production. As a result, many companies fail to earn the value that comes from their efforts and investments. In production environment data continuously comes, result are computed and models are frequently trained. The challenges faced by companies fall into four categories:  Small Data Teams: They mostly use small data, often don’t retrain models and business team is involved in a development project. 

Packagers: Often build their framework from scratch and practice informal A/B testing , generally not involved with the business team

Industrialization Maniacs: These teams are IT led and automated process for deployment and maintenance , business team are not involved in monitoring and development

The Big Data Lab : Uses more complex technologies , business teams are involved before and after deployment of data product

Companies should understand that working in production is different than working with SQL databases in development , moreover real time learning and multi-language environments will make your process complex. Also a strong collaboration between business and IT teams will increase your efficiency. Read more at : http://dataconomy.com/2017/02/value-from-data-science-production/

 

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