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Found 5 papers, 4 papers with code
The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development
To address these problems, we introduce the Machine Learning Bazaar, a new framework for developing machine learning and automated machine learning software systems.
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.
Solving the "false positives" problem in fraud prediction
In this paper, we present an automated feature engineering based approach to dramatically reduce false positives in fraud prediction.
Deep Feature Synthesis: Towards Automating Data Science Endeavors
In this paper, we develop the Data Science Machine, which is able to derive predictive models from raw data automatically.
