The Technological Emergence of AutoML: A Survey of Performant Software
and Applications in the Context of Industry
- URL: http://arxiv.org/abs/2211.04148v1
- Date: Tue, 8 Nov 2022 10:42:08 GMT
- Title: The Technological Emergence of AutoML: A Survey of Performant Software
and Applications in the Context of Industry
- Authors: Alexander Scriven, David Jacob Kedziora, Katarzyna Musial, Bogdan
Gabrys
- Abstract summary: Automated/Autonomous Machine Learning (AutoML/AutonoML) is a relatively young field.
This review makes two primary contributions to knowledge around this topic.
It provides the most up-to-date and comprehensive survey of existing AutoML tools, both open-source and commercial.
- Score: 72.10607978091492
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With most technical fields, there exists a delay between fundamental academic
research and practical industrial uptake. Whilst some sciences have robust and
well-established processes for commercialisation, such as the pharmaceutical
practice of regimented drug trials, other fields face transitory periods in
which fundamental academic advancements diffuse gradually into the space of
commerce and industry. For the still relatively young field of
Automated/Autonomous Machine Learning (AutoML/AutonoML), that transitory period
is under way, spurred on by a burgeoning interest from broader society. Yet, to
date, little research has been undertaken to assess the current state of this
dissemination and its uptake. Thus, this review makes two primary contributions
to knowledge around this topic. Firstly, it provides the most up-to-date and
comprehensive survey of existing AutoML tools, both open-source and commercial.
Secondly, it motivates and outlines a framework for assessing whether an AutoML
solution designed for real-world application is 'performant'; this framework
extends beyond the limitations of typical academic criteria, considering a
variety of stakeholder needs and the human-computer interactions required to
service them. Thus, additionally supported by an extensive assessment and
comparison of academic and commercial case-studies, this review evaluates
mainstream engagement with AutoML in the early 2020s, identifying obstacles and
opportunities for accelerating future uptake.
Related papers
- Surveying the MLLM Landscape: A Meta-Review of Current Surveys [17.372501468675303]
Multimodal Large Language Models (MLLMs) have become a transformative force in the field of artificial intelligence.
This survey aims to provide a systematic review of benchmark tests and evaluation methods for MLLMs.
arXiv Detail & Related papers (2024-09-17T14:35:38Z) - RelevAI-Reviewer: A Benchmark on AI Reviewers for Survey Paper Relevance [0.8089605035945486]
We propose RelevAI-Reviewer, an automatic system that conceptualizes the task of survey paper review as a classification problem.
We introduce a novel dataset comprised of 25,164 instances. Each instance contains one prompt and four candidate papers, each varying in relevance to the prompt.
We develop a machine learning (ML) model capable of determining the relevance of each paper and identifying the most pertinent one.
arXiv Detail & Related papers (2024-06-13T06:42:32Z) - Position: A Call to Action for a Human-Centered AutoML Paradigm [83.78883610871867]
Automated machine learning (AutoML) was formed around the fundamental objectives of automatically and efficiently configuring machine learning (ML)
We argue that a key to unlocking AutoML's full potential lies in addressing the currently underexplored aspect of user interaction with AutoML systems.
arXiv Detail & Related papers (2024-06-05T15:05:24Z) - A Survey on Large Language Model based Autonomous Agents [105.2509166861984]
Large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence.
This paper delivers a systematic review of the field of LLM-based autonomous agents from a holistic perspective.
We present a comprehensive overview of the diverse applications of LLM-based autonomous agents in the fields of social science, natural science, and engineering.
arXiv Detail & Related papers (2023-08-22T13:30:37Z) - Benchmarking Automated Machine Learning Methods for Price Forecasting
Applications [58.720142291102135]
We show the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions.
Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part.
We show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts.
arXiv Detail & Related papers (2023-04-28T10:27:38Z) - Interactive Machine Learning: A State of the Art Review [0.0]
We provide a comprehensive analysis of the state-of-the-art of interactive machine learning (iML)
Research works on adversarial black-box attacks and corresponding iML based defense system, exploratory machine learning, resource constrained learning, and iML performance evaluation are analyzed.
arXiv Detail & Related papers (2022-07-13T13:43:16Z) - The Roles and Modes of Human Interactions with Automated Machine
Learning Systems [7.670270099306412]
Automated machine learning (AutoML) systems continue to progress in both sophistication and performance.
It becomes important to understand the how' and why' of human-computer interaction (HCI) within these frameworks.
This review serves to identify key research directions aimed at better facilitating the roles and modes of human interactions with both current and future AutoML systems.
arXiv Detail & Related papers (2022-05-09T09:28:43Z) - Automated Machine Learning, Bounded Rationality, and Rational
Metareasoning [62.997667081978825]
We will look at automated machine learning (AutoML) and related problems from the perspective of bounded rationality.
Taking actions under bounded resources requires an agent to reflect on how to use these resources in an optimal way.
arXiv Detail & Related papers (2021-09-10T09:10:20Z) - Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations.
Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
arXiv Detail & Related papers (2021-01-15T10:43:10Z) - AutoML to Date and Beyond: Challenges and Opportunities [30.60364966752454]
AutoML tools aim to make machine learning accessible for non-machine learning experts.
We introduce a new classification system for AutoML systems.
We lay out a roadmap for the future, pinpointing the research required to further automate the end-to-end machine learning pipeline.
arXiv Detail & Related papers (2020-10-21T06:08:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.