Workshop Overview

Early in 2020, a Technology Magazine article argued “Why 2020 will be the Year of Automated Machine Learning” - reasoning that "AutoML represents the next stage in ML’s evolution, promising to help non-tech companies access the capabilities they need to quickly and cheaply build ML applications.” What could not be foreseen is how 2020 saw the focus of AutoML, like many other efforts, turn to COVID-19: to predict patient survival, to predict patient mortality, to model the progression of COVID-19 deaths, and related healthcare and medical diagnosis efforts. AutoML continues to generate very current and widespread attention regarding appropriate uses, current capabilities, limitations, challenges, and future potential (Forbes, 2021-02-23; Waring, J. et. al., 2020).

The debates continue regarding the level to which data science can and should be automated, the level of machine learning knowledge and expertise needed to build quality models, and the where and when manual intervention is necessary, yet the development and application of approaches and tools to automate repeated tasks continues to increase. 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 Sponsors:

• RTP ACM Chapter

• IEEE SMC Human Perception in Multimedia Computing

Call for Content

Agenda