[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117901-en":3,"doc-seo-117901-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},117901,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","An investigation of challenges in the machine learning lifecycle and the importance of MLOps: A survey","Machine learning has gained broad attention due to advances in AI and data science, and to the proliferation of popular tools and cloud services that lower barriers for practitioners without deep statistical or mathematical training. Despite this momentum, operationalizing and sustaining machine learning-centered products remains difficult, leading to unmet business expectations. This paper identifies challenges faced by data scientists when building ML-centric products and examines how MLOps practices help address them, based on survey responses from 66 Brazilian professionals.","An investigation of challenges in the machine learning lifecycle and the importance of MLOps: A survey  \nBruno Faustino Amorim  \nUniversidade Tecnológica Federal do Paraná Dois Vizinhos, Paraná, Brasil [bamorim@alunos.utfpr.edu.br](bamorim@alunos.utfpr.edu.br)  \nAlinne C. Corrêa Souza  \nUniversidade Tecnológica Federal do Paraná Dois Vizinhos, Paraná, Brasil [alinnesouza@utfpr.edu.br](alinnesouza@utfpr.edu.br)  \nLincoln M. Costa  \nUniversidade Federal do Rio de Janeiro Rio de Janeiro, Rio de Janeiro, Brasil [costa@cos.ufrj.br](costa@cos.ufrj.br)  \nABSTRACT  \nQuite recently, considerable attention has been paid to developing artificial intelligence and data science areas. This has been driven by scientific advances and the growing number of software and services that are popularizing machine learning techniques and algorithms and driving people with less knowledge in areas such as statistics and mathematics to create their predictive models. Asa result, the machine learning field is no longer only scientific and has aroused the interest of companies from different domains. These events led to the emergence of multiple tools such as ScikitLearn, Tensorflow, Keras, Pycaret, and a vast number of cloud-based machine learning services that provide an acceleration in the development of predictive models at speeds never seen. However, many challenges remain in operationalizing and maintaining machine learning-centered products, making many business initiatives frustrated. In this scenario, practical experience shows that machine learning is only a slice of a more extensive set of practices and technologies necessary to build solutions in this area. In this paper, the main goal is to identify the challenges currently faced by data scientists in developing Machine Learning-centric products and how Machine Learning Operations can support overcoming them. For this purpose, a survey was conducted that collected answers from 66 Brazilian professionals in data science. From the challenges identified, the importance of Machine Learning Operations practices as an integrated part of the Machine Learning lifecycle was explored. Finally, this work contributes to filling the gap in Machine Learning Operations in daily activities involving data science and advancing this research field in Brazil.  \nCCS CONCEPTS  \n• Computing methodologies → Machine learning.  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions [from permissions@acm.org](from permissions@acm.org).  \nComputer on the Beach ’23, 30 de março a 01 de abril, 2023, Florianópolis, SC © 2023 Association for Computing Machinery.  \nACM ISBN 978-x-xxxx-xxxx-x/YY/MM. . . $15.00 [https://doi.org/10.1145/nnnnnnn.nnnnnnn](https://doi.org/10.1145/nnnnnnn.nnnnnnn)  \nFrancisco Carlos M. Souza  \nUniversidade Tecnológica Federal do Paraná Dois Vizinhos, Paraná, Brasil [franciscosouza@utfpr.edu.br](franciscosouza@utfpr.edu.br)  \nKEYWORDS  \nMachile Learning, MLOps  \nACM Reference Format:  \nBruno Faustino Amorim, Alinne C. Corrêa Souza, Lincoln M. Costa, and Francisco Carlos M. Souza. 2023. An investigation of challenges in the machine learning lifecycle and the importance of MLOps: A survey. In Proceedings of Computer on the Beach (Computer on the Beach ’23). ACM, New York, NY, USA, 8 pages. [https://doi.org/10.1145/nnnnnnn.nnnnnnn](https://doi.org/10.1145/nnnnnnn.nnnnnnn)  \n1 INTRODUÇÃO  \nO crescente aumento no volume dos dados, somado aos avanços acadêmicos, direcionou empresas de diversos setores a investir em in","cbCaitTzJQRxigVP","https://ap.wps.com/l/cbCaitTzJQRxigVP","pdf",1005860,1,"English","en",105,"# Abstract\n# Introduction\n## Machine learning adoption and expectations","[{\"question\":\"What problem does the paper focus on?\",\"answer\":\"It focuses on challenges encountered across the machine learning lifecycle and the importance of MLOps for supporting data scientists in ML-centric product development.\"},{\"question\":\"How was the study conducted?\",\"answer\":\"The study used a survey collecting responses from 66 Brazilian data science professionals.\"},{\"question\":\"Why are many business initiatives frustrated even with advanced ML tools?\",\"answer\":\"Because success depends not only on highly accurate predictive models, but also on practical operationalization and maintenance within broader software ecosystems.\"}]","An investigation of challenges in the machine learning lifecycle and the importance of MLOps: A survey | 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