17th International Conference on Data, AI and Machine Learning Systems (DAIMLS 2026)

Data Systems and Database Technologies

• Distributed, parallel and cloud native databases
• Serverless databases and elastic OLTP/OLAP
• Multi model databases (graph, document, time series, vector, key value)
• Query processing, optimization and self-driving databases
• In memory, NVM and CXL accelerated data systems
• Hardware accelerated data systems (GPU, FPGA, DPU, SmartNIC)
• AI native storage engines and computer architectures


Cloud Platforms and Large Scale Data Infrastructure

• Cloud native data platforms and cloud databases
• Multi cloud and cross cloud data federation
• Data lakes, lakehouses and unified analytics
• Scalable data processing engines (Spark, Flink, Ray, Dask)
• Disaggregated storage/compute architectures
• Geo distributed data and global inference systems
• Energy efficient and carbon aware data systems
• Cloud cost optimization and FinOps for AI/ML

AI Systems and Machine Learning Infrastructure

• Distributed ML training systems and large scale model training
• GPU/TPU/DPU accelerated ML infrastructure
• ML compilers, optimization and deep learning systems
• ML driven system optimization and learned components
• Feature stores, online ML and real time inference
• Memory efficient AI systems (quantization, pruning, distillation)
• Continual learning and lifelong model systems
• Mixture of Experts (MoE) training and serving systems

LLM, RAG and Intelligent Retrieval Systems

• Retrieval augmented generation (RAG) pipelines
• Vector databases and embedding management
• Hybrid retrieval (vector + graph + keyword)
• High throughput retrieval for LLM inference
• LLM augmented data systems (text to SQL, NL querying)
• RAG orchestrated workflows and pipelines
• Index selection and optimization for LLM/RAG workloads

Data Centric AI, Synthetic Data and ML Data Quality

• Data validation, debugging and quality for ML
• Weak supervision and programmatic labeling
• Data augmentation systems
• Synthetic data generation and evaluation
• Privacy preserving synthetic data pipelines
• Dataset versioning and curation for ML/LLMs
• Data contracts for AI/ML