AutoML to Date and Beyond: Challenges and Opportunities

Shubhra Kanti Karmaker Santu, Md Mahadi Hassan, Micah J. Smith, Lei Xu, Chengxiang Zhai, Kalyan Veeramachaneni

Research output: Contribution to journalReview articlepeer-review


As big data becomes ubiquitous across domains, and more and more stakeholders aspire to make the most of their data, demand for machine learning tools has spurred researchers to explore the possibilities of automated machine learning (AutoML). AutoML tools aim to make machine learning accessible for non-machine learning experts (domain experts), to improve the efficiency of machine learning, and to accelerate machine learning research. But although automation and efficiency are among AutoML's main selling points, the process still requires human involvement at a number of vital steps, including understanding the attributes of domain-specific data, defining prediction problems, creating a suitable training dataset, and selecting a promising machine learning technique. These steps often require a prolonged back-and-forth that makes this process inefficient for domain experts and data scientists alike and keeps so-called AutoML systems from being truly automatic. In this review article, we introduce a new classification system for AutoML systems, using a seven-tiered schematic to distinguish these systems based on their level of autonomy. We begin by describing what an end-to-end machine learning pipeline actually looks like, and which subtasks of the machine learning pipeline have been automated so far. We highlight those subtasks that are still done manually - generally by a data scientist - and explain how this limits domain experts' access to machine learning. Next, we introduce our novel level-based taxonomy for AutoML systems and define each level according to the scope of automation support provided. Finally, we lay out a roadmap for the future, pinpointing the research required to further automate the end-to-end machine learning pipeline and discussing important challenges that stand in the way of this ambitious goal.

Original languageEnglish (US)
Article number175
JournalACM Computing Surveys
Issue number8
StatePublished - Nov 2022


  • Additional Key Words and PhrasesAutomated machine learning
  • democratization of artificial intelligence
  • interactive data science
  • predictive analytics

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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