DBML 2026 Workshop, held in conjunction with ICDE 2026 in Montréal, Canada, explores the growing synergy between databases and machine learning.

Advances in data management techniques have become essential for building robust, scalable ML systems. Applications range from data preparation and cleaning to feature engineering and managing the ML lifecycle. The recent rise of LLMs and RAG has only intensified demand for high-performance data infrastructure. Modern AI systems increasingly rely on vector databases and scalable model serving. Multimodal AI adds further requirements for storing and querying images, audio, and video.

In the opposite direction, ML techniques are now incorporated as core components of database systems: query optimization, indexing, storage layout, and self-tuning. Long-standing challenges like cardinality estimation, operator and plan selection, and resource management - traditionally handled via human knowledge or heuristics - increasingly benefit from learned models.

DBML 2026 brings together researchers and practitioners working at this intersection. We welcome work combining DB and ML strengths, ranging from foundational techniques and system design to practical applications and real-world deployments, including ML for scientific and data-intensive domains.

Information about previous editions can be found at DBML 2025 DBML 2024, DBML 2023, and DBML 2022.

For questions regarding the workshop, please contact: dbml26@googlegroups.com.
Topics of Interest
ML for Data Management and DBMS

Learned data discovery, cleaning, and transformation
ML-enabled data exploration and discovery in data lakes and lakehouses
Learned database design, configuration, and tuning
ML for query optimization, indexing, and storage/layout decisions
Natural language interfaces for data (querying, exploration, summarization, assistants)
Pretrained, foundation, and LLM-based models for data management
Representation learning for data cleaning, preprocessing, and integration
Benchmarking and evaluation of ML-enhanced data management and DBMS components

Data Management for ML and AI Systems

Data collection, preparation, and governance for ML/LLM/RAG applications
Data quality, robustness, provenance, and lineage for ML workflows
Systems and storage for efficient training, inference, and model serving
Vector databases, indexing, and hybrid query processing for embeddings
Management of multimodal data (text, images, audio, video, etc.) for AI applications
DB-inspired techniques for modeling, storage, and provenance of ML and AI artifacts