{"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/machine-learning-20-engineering-data-driven","title":"Machine learning 2.0 : Engineering Data Driven AI Products","arxiv_id":"1807.00401","date":"2018-07-01","proceeding":null,"authors":["James Max Kanter","Benjamin Schreck","Kalyan Veeramachaneni"],"abstract":"ML 2.0: In this paper, we propose a paradigm shift from the current practice\nof creating machine learning models - which requires months-long discovery,\nexploration and \"feasibility report\" generation, followed by re-engineering for\ndeployment - in favor of a rapid, 8-week process of development, understanding,\nvalidation and deployment that can executed by developers or subject matter\nexperts (non-ML experts) using reusable APIs. This accomplishes what we call a\n\"minimum viable data-driven model,\" delivering a ready-to-use machine learning\nmodel for problems that haven't been solved before using machine learning. We\nprovide provisions for the refinement and adaptation of the \"model,\" with\nstrict enforcement and adherence to both the scaffolding/abstractions and the\nprocess. We imagine that this will bring forth the second phase in machine\nlearning, in which discovery is subsumed by more targeted goals of delivery and\nimpact.","url_abs":"http://arxiv.org/abs/1807.00401v1","url_pdf":"http://arxiv.org/pdf/1807.00401v1.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":"machine-learning-20-engineering-data-driven","repo_url":"https://github.com/Featuretools/featuretools-docker","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"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}