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Found 2 papers, 1 papers with code
Prediction Factory: automated development and collaborative evaluation of predictive models
In a head-to-head trial, reports generated utilizing full data science automation interface reports were funded 57. 5% of the time, while the ones that used baseline automation were only funded 42. 5% of the time.
Machine learning 2.0 : Engineering Data Driven AI Products
ML 2. 0: In this paper, we propose a paradigm shift from the current practice of creating machine learning models - which requires months-long discovery, exploration and "feasibility report" generation, followed by re-engineering for deployment - in favor of a rapid, 8-week process of development, understanding, validation and deployment that can executed by developers or subject matter experts (non-ML experts) using reusable APIs.
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