The field of deep learning is moving towards developing more efficient and scalable systems. Researchers are exploring various approaches to improve the efficiency of deep learning models, including architecture design, model compression, and optimization. A key direction is the development of principled approximation methods that can tackle computationally hard problems in a scalable and efficient manner. Another area of focus is the design of novel neural architecture search algorithms that can efficiently explore the search space and identify optimal architectures. Additionally, there is a growing interest in developing in-database model management systems that can efficiently store and utilize deep learning models. Noteworthy papers in this area include: Principled Approximation Methods for Efficient and Scalable Deep Learning, which proposes novel approximations for model compression and optimization. A Continuous Encoding-Based Representation for Efficient Multi-Fidelity Multi-Objective Neural Architecture Search, which presents an adaptive Co-Kriging-assisted multi-fidelity multi-objective NAS algorithm. NeurStore: Efficient In-database Deep Learning Model Management System, which introduces a novel in-database model management system for efficient storage and utilization of deep learning models.