Over-the-Wall Data Science and How to Avoid Its Pitfalls

Over-the-wall data science is a common organizational pattern for deploying data science team output to production systems. A data scientist develops an algorithm, a model, or a machine learning pipeline, and then an engineer, often from another team, is responsible for putting the data scientist’s code in production.

Such a pattern of development attempts to solve for the following: Quality: We want production code to be of high quality and maintained by engineering teams. Since most data scientists are not great software engineers, they are not expected to write end-to-end production-quality code. Resource Allocation: Building and maintaining production systems requires special expertise, and data scientists can contribute more value solving problems for which they were trained rather than spend the time acquiring such expertise.

Author: Sergei Izrailev

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