{"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/neuraxle-a-python-framework-for-neat-machine","title":"Neuraxle - A Python Framework for Neat Machine Learning Pipelines","arxiv_id":null,"date":"2019-09-01","proceeding":null,"authors":["Chevalier","Guillaume and Brillant","Alexandre and Hamel","Eric"],"abstract":"Code Machine Learning Pipelines - The Right Way. The present new framework is for building neat pipelines, providing the right abstractions to both ease research, development, and deployment of your ML applications. Most open-source frameworks for machine learning offer abstractions that hardly allow automatic machine learning, code reuse, and deploying models to productions. The hyperparameter search space is often strongly coupled to the whole pipeline rather than being decoupled of the pipeline and only coupled to the individual estimator objects: the pipeline steps. The need for an open-source and scaleable pipeline library is strong.","url_abs":"https://www.researchgate.net/publication/337002011_Neuraxle_-_A_Python_Framework_for_Neat_Machine_Learning_Pipelines","url_pdf":"https://www.researchgate.net/publication/337002011_Neuraxle_-_A_Python_Framework_for_Neat_Machine_Learning_Pipelines","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":"neuraxle-a-python-framework-for-neat-machine","repo_url":"https://github.com/Neuraxio/Neuraxle","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"neuraxle-a-python-framework-for-neat-machine","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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}