Your Checklist to Get Data Science Implemented in Production

Building a data science project and training a model is only the first step. Getting that model to run in the production environment is where companies often fail. Indeed, implementing a model into the existing data science and IT stack is very complex for many companies. A disconnect between the tools and techniques used in the design environment and the live production environment. For over a year we surveyed thousands of companies from all types of industries and data science advancement on how they managed to overcome these difficulties and analyzed the results.