A critical challenge of data science projects is getting everyone on the same page in terms of project challenges, responsibilities, and methodologies. More often than not, there is a disconnect between the worlds of development and production. Some teams may choose to re-code everything in an entirely different language while others may make changes to core elements, such as testing procedures, backup plans, and programming languages. Transitioning a data product into production could become a nightmare as different opinions and methods vie for supremacy, resulting in projects that needlessly drag on for months beyond promised deadlines. Successfully building a data product and then deploying it into production is not an easy task — it becomes twice as hard when teams are isolated and playing by their own rules. Putting Data Science In Production
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