DEEM: Workshop on Data Management for End-to-End Machine Learning @ ACM SIGMOD 2026

About

DEEM brings together researchers and practitioners at the intersection of applied machine learning, data management and systems research, with the goal to discuss the arising data management issues in ML application scenarios. The DEEM workshop will be held on Sunday, May 31st, in conjunction with SIGMOD/PODS 2026. The workshop will be held in-person in Bengaluru.

The workshop solicits regular research papers (8 pages plus unlimited references) describing preliminary or completed research results, as well as short papers (up to 4 pages) such as reports on applications and tools or preliminary results, interesting use cases, problems, datasets, benchmarks, visionary ideas, and descriptions of system components and tools related to end-to-end ML pipelines. Submissions should follow the guidelines as for SIGMOD, i.e. use the sigconf template for the ACM proceedings format.

Follow us on twitter @deem_workshop, bluesky @deem-workshop.bsky.social, or contact the organizers via email. We also provide archived websites of previous versions of the workshop: DEEM 2017, DEEM 2018, DEEM 2019, DEEM 2020, DEEM 2021, DEEM 2022, DEEM 2023, DEEM 2024, and DEEM 2025.

Important Dates

Submission deadline: February 27, 5pm Pacific Time
Submission website: https://cmt3.research.microsoft.com/DEEM2026
Notification of acceptance: April 3, 2026
Final papers due: April 17, 2026
Workshop: Sunday, May 31, 2026

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Call for Papers

Applying 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 large language models (LLMs).

For 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. After 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 in such topics.

Additionally, 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 if we do not consider the lifecycle stages upstream from model training and deployment. DEEM welcomes research on providing system-level support to data scientists who wish to develop and deploy responsible machine learning methods.

DEEM aims to bring together researchers and practitioners at the intersection of applied machine learning, data management and systems research, with the goal to discuss the arising data management issues in ML application scenarios.

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Topics of Interest

Areas of particular interest for the workshop include (but are not limited to):

Data Management in Machine Learning Applications
Definition, Execution and Optimization of Complex Machine Learning Pipelines
Systems for ML, e.g. for Managing the Lifecycle of ML Models, Efficient Hyper-parameter Search, or Feature Selection
Machine Learning Services in the Cloud
Modeling, Storage and Provenance of Machine Learning Artifacts
Integration of Machine Learning and Dataflow Systems
Integration of Machine Learning and ETL Processing
Definition and Execution of Complex Ensemble Predictors
Sourcing, Labeling, Integrating, and Cleaning Data for Machine Learning
MLOps, Data Validation, and Model Debugging Techniques
Privacy-preserving Machine Learning
Benchmarking of Machine Learning Applications
Responsible Data Management
Transparency and Accountability of Machine-Assisted Decision Making
Impact of Data Quality and Data Preprocessing on the Fairness of ML Predictions
Horror stories, Anecdotes, and Lessons Learned on data management for ML
Data management for multimodal ML
Vector Databases for Retrieval and Systems for Retrieval Augmented Generation
ML for data management for ML
Data Management challenges, e.g. responsible data management, for LLMs

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Submission

We invite submissions in the following two tracks:

Regular Papers (research and industrial papers; up to 8, plus unlimited references)
Short Papers (preliminary results, interesting use cases, problems, datasets, benchmarks, visionary ideas, system designs, and descriptions of system components and tools; up to 4 pages)

Papers of any category can have at most 2 additional appendix pages.
Authors are requested to prepare submissions following the ACM proceedings format consistent with the SIGMOD submission guidelines. Please use the latest ACM paper format with the sigconf template. DEEM is a single-anonymous workshop, authors must include their names and affiliations on the manuscript cover page.

Submission website: https://cmt3.research.microsoft.com/DEEM2026

The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.

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