{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/atm-a-distributed-collaborative-scalable","title":"ATM: A distributed, collaborative, scalable system for automated machine learning","arxiv_id":null,"date":"2017-12-11","proceeding":"2017 IEEE International Conference on Big Data (Big Data) 2017 12","authors":["Thomas Swearingen","Will Drevo","Bennett Cyphers","Alfredo Cuesta-Infante","Arun Ross","Kalyan Veeramachaneni"],"abstract":"In this paper, we present Auto-Tuned Models, or ATM, a distributed, collaborative, scalable system for automated machine learning. Users of ATM can simply upload a dataset, choose a subset of modeling methods, and choose to use ATM’s hybrid Bayesian and multi-armed bandit optimization system. The distributed system works in a load balanced fashion to quickly deliver results in the form of ready-to-predict models, confusion matrices, cross-validation results, and training timings. By automating hyperparameter tuning and model selection, ATM returns the emphasis of the machine learning workflow to its most irreducible part: feature engineering. We demonstrate the usefulness of ATM on 420 datasets from OpenML and train over 3 million classifiers. Our initial results show ATM can beat human-generated solutions for 30% of the datasets, and can do so in 1/100th of the time.","url_abs":"https://ieeexplore.ieee.org/document/8257923","url_pdf":"https://dai.lids.mit.edu/wp-content/uploads/2018/02/atm_IEEE_BIgData-9-1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"atm-a-distributed-collaborative-scalable","repo_url":"https://github.com/HDI-Project/ATM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"atm-a-distributed-collaborative-scalable","repo_url":"https://github.com/jim-schwoebel/allie","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"automl","task_name":"AutoML"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"hyperparameter-optimization","task_name":"Hyperparameter Optimization"},{"task_slug":"model-selection","task_name":"Model Selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}