In this paper, we propose a paradigm shift from the current practice of creating machine learning models that requires months-long discovery, exploration and “feasibility report” generation, followed by re-engineering for deployment, in favor of a rapid 8 week long process of development, understanding, validation and deployment that can executed by developers or subject matter experts (non-ML experts) using reusable APIs. It accomplishes what we call a “minimum viable data-driven model,” delivering a ready-to-use machine learning model for problems that haven’t been solved before using machine learning…

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