[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121547-en":3,"doc-seo-121547-105":30,"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121547,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Ninth Workshop on Data Management for End-to-End Machine Learning (DEEM) - Proceedings Overview","The DEEM’25 workshop (Data Management for End-to-End Machine Learning) takes place on Friday, June 27, 2025 in conjunction with SIGMOD/PODS 2025, bringing together researchers and practitioners across applied machine learning, data management, and systems research. The workshop calls for regular research papers and short papers covering end-to-end ML deployment experience, pipeline design, optimization, system support, and responsible ML lifecycle concerns. This year it received 18 high-quality submissions across diverse DEEM-relevant topics.","Ninth Workshop on Data Management for End-to-End Machine  \nLearning (DEEM)  \nStefan Grafberger  \nBIFOLD & TU Berlin Berlin, Germany  \nMatteo Interlandi  \nMicrosoft Los Angeles, USA  \nMadelon Hulsebos  \nCWI Amsterdam, Netherlands  \nShreya Shankar UC Berkeley Berkeley, USA  \nAbstract  \nThe DEEM’25 workshop (Data Management for End-to-End Machine Learning) is held on Friday, June 27th, in conjunction with SIGMOD/PODS 2025 . DEEM brings together researchers and practitioners at the intersection of applied machine learning, data management, and systems research, with the goal of discussing the arising data management issues in ML application scenarios. The workshop solicits regular research papers (8 pages) describing preliminary and ongoing research results, including industrial experience reports of end-to-end ML deployments, related to DEEM topics. In addition, DEEM 2025 has a category for short papers (4 pages) as a forum for sharing interesting use cases, problems, datasets, benchmarks, visionary ideas, system designs, preliminary results, and descriptions of system components and tools related to end-to-end ML pipelines. This year, the workshop received 18 high-quality submissions on diverse topics relevant to DEEM.  \nACM Reference Format:  \nStefan Grafberger, Madelon Hulsebos, Matteo Interlandi, and ShreyaShankar.  \n2025. Ninth Workshop on Data Management for End-to-End Machine Learning (DEEM) . In Companion of the 2025 International Conference on Management of Data (SIGMOD-Companion ’25), June 22–27, 2025, Berlin, Germany. ACM, New York, NY, USA, 2 pages. [https://doi.org/10.1145/3722212.3724483](https://doi.org/10.1145/3722212.3724483)  \n1 Introduction  \nApplying Machine Learning (ML) in real-world scenarios is a challenging task. In recent years, the main focus of the data management community has been on creating systems and abstractions for the efficient training of ML models on large datasets. However, model training is only one of many steps in an end-to-end ML application, and a number of orthogonal data management problems arise from the large-scale use of ML and increased adoption of large language models (LLMs) .  \nFor example, data preprocessing and feature extraction workloads may be complicated and require simultaneous execution of relational and linear algebraic operations. Next, model selection may involve searching many combinations of model architectures, features, and hyper-parameters to find the best-performing model.  \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 third-party components of this work must be honored. For all other uses, contact the owner/author(s) .  \nSIGMOD-Companion ’25, Berlin, Germany  \n© 2025 Copyright held by the owner/author(s) .  \nACM ISBN 979-8-4007-1564-8/2025/06  \n[https://doi.org/10.1145/3722212.3724483](https://doi.org/10.1145/3722212.3724483)  \nAfter model training, the resulting model may have to be deployed and integrated into business workflows and require lifecycle management using metadata and lineage. As a further complication, the resulting system may have to take into account a heterogeneous audience, ranging from domain experts without programming skills to data engineers and statisticians who develop custom algorithms. Many such challenges are human or engineer-centered (e.g., monitoring ML pipelines, leveraging LLMs for domain-specific tasks at scale), and DEEM uniquely encourages submissions on such topics.  \nAdditionally, the importance of incorporating ethics and legal compliance into machine-assisted decision-making is being broadly recognized. Critical opportunities for improving data quality and representativeness, controlling for bias, and allowing humans to oversee and impact computational processes are missed ","cbCaikgYsoP1tcKO","https://ap.wps.com/l/cbCaikgYsoP1tcKO","pdf",843058,1,2,"English","en",105,"# Introduction\n# Topics of Interest","[{\"question\":\"What is the purpose of the DEEM’25 workshop?\",\"answer\":\"DEEM’25 focuses on data management issues arising in end-to-end machine learning and LLM application scenarios, connecting researchers and practitioners to discuss relevant challenges and solutions.\"},{\"question\":\"What kinds of papers does DEEM solicit?\",\"answer\":\"The workshop solicits regular research papers (8 pages) and short papers (4 pages), including preliminary or ongoing results, industrial experience reports, use cases, datasets, benchmarks, system designs, and tool or component descriptions for end-to-end ML pipelines.\"},{\"question\":\"Which topics are highlighted as areas of particular interest?\",\"answer\":\"Topics include data management in ML applications, defining/executing/optimizing complex ML pipelines, lifecycle management and efficient hyper-parameter search systems, ML services in the cloud, and modeling/storage/provenance of ML artifacts as well as integration with dataflow and ETL.\"}]","Ninth Workshop on Data Management for End-to-End Machine Learning (DEEM) - 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