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No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark

466 views · Published 12 June 2017 · 28:45 · Indexed 25 October 2026

Channel: Databricks · 2017 · Science & Technology

"Building accurate machine learning models has been an art of data scientists, i.e., algorithm selection, hyper parameter tuning, feature selection and so on. Recently, challenges to breakthrough this “black-arts” have got started. We have developed a Spark-based automatic predictive modeling system. The system automatically searches the best algorithm, the best parameters and the best features without any manual work. In this talk, we will share how the automation system is designed to exploit attractive advantages of Spark. Our evaluation with real open data demonstrates that our system could explore hundreds of predictive models and discovers the highly-accurate predictive model in minutes on a Ultra High Density Server, which employs 272 CPU cores, 2TB memory and 17TB SSD in 3U chassis. We will also share open challenges to learn such a massive amount of models on Spark, particularly from reliability and stability standpoints. With Masato Asahara and Ryohei Fujimaki.

Session hashtag: #SFds5"

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No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark

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