Search Results for author: James Max Kanter

Found 5 papers, 4 papers with code

The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development

8 code implementations22 May 2019 Micah J. Smith, Carles Sala, James Max Kanter, Kalyan Veeramachaneni

To address these problems, we introduce the Machine Learning Bazaar, a new framework for developing machine learning and automated machine learning software systems.

AutoML Bayesian Optimization +1

Prediction Factory: automated development and collaborative evaluation of predictive models

no code implementations29 Nov 2018 Gaurav Sheni, Benjamin Schreck, Roy Wedge, James Max Kanter, Kalyan Veeramachaneni

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

1 code implementation1 Jul 2018 James Max Kanter, Benjamin Schreck, Kalyan Veeramachaneni

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.

BIG-bench Machine Learning

Solving the "false positives" problem in fraud prediction

1 code implementation20 Oct 2017 Roy Wedge, James Max Kanter, Santiago Moral Rubio, Sergio Iglesias Perez, Kalyan Veeramachaneni

In this paper, we present an automated feature engineering based approach to dramatically reduce false positives in fraud prediction.

Automated Feature Engineering Feature Engineering +1

Deep Feature Synthesis: Towards Automating Data Science Endeavors

1 code implementation DSAA 2015 2015 James Max Kanter, Kalyan Veeramachaneni

In this paper, we develop the Data Science Machine, which is able to derive predictive models from raw data automatically.

Automated Feature Engineering

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