Workshop Overview

There have been many debates in recent years about the need and the ability to automate data mining and machine learning tasks. A recent blog post titled “Data Scientists Need More Automation” discusses the repeated efforts required to configure and run services or scripts on a network of machines. Other discussions ask, “Can We Automate Data Mining?,” arguing that many tasks performed by data scientists “cannot be automated and need manual intervention”; in other words, expertise is needed for each individual case, requiring clear understanding of the business and the data. The advancement, education, and adoption of data mining and machine learning practices require a transformation of theory to application, and feedback from application to theory. The development of tools to automate data mining efforts fosters this transformation and feedback and also promotes the development of standards and the adoption of these standards. Automated standards enable researchers and practitioners to better communicate, sharing successes and challenges in a more consistent common language. In an age of software as a service and ever-increasing scalability requirements, standards are necessary. Consistent adoption, application, and communication in turn promote research and refinement of the automated strategies and growth of the community. To keep pace with the rapidly increasing volume and rate of data generation, standardization and automating of data mining activities are critical. The challenges that must be discussed relate to the boundaries of automated tasks and individual attention needed for each unique business and data scenario.

The goals of the AutoML workshop are:

· To identify opportunities and challenges for automation in machine learning

· To provide an opportunity for researchers to discuss best practices for automation in machine learning, potentially leading to definition of standards

· To provide a forum for researchers to speak out and debate on different ideas in the area of automation in machine learning

Technical Sponsor: IEEE SMC Human Perception in Multimedia Computing

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