Saturday 08 March 2025
Deep learning systems have revolutionized many fields, but they also present unique challenges in software engineering. Achieving high-quality outcomes while balancing efficiency and resource utilization is crucial for domains like Edge AI, where tasks are executed directly on devices.
To address these quality challenges, researchers have been exploring the role of machine learning operations (MLOps) and domain knowledge in deep learning system development. MLOps practices, such as monitoring and experiment tracking, can create transparent and reproducible experimentation environments that enable teams to assess the impact of design decisions on quality attributes.
One study investigated how embedding domain knowledge into a deep learning model’s design can significantly improve its performance. By leveraging context-specific rules or constraints, domain-aware algorithms can achieve higher accuracy and efficiency. The researchers developed several domain-aware approaches, including an intelligent algorithm (IA) that outperformed traditional methods in terms of accuracy, latency, and energy consumption.
The study also highlighted the importance of monitoring energy consumption to improve quality attributes like resource utilization. Software-based tools, such as Codecarbon, can be integrated into code with minimal effort, providing detailed measurements of energy consumption. However, these tools may not work seamlessly with all CPUs, requiring additional software or hardware solutions.
The researchers found that manual data-related tasks are adequate for the early stages of a deep learning system’s development but become impractical and time-consuming as the system grows in complexity. Automating data processing pipelines can abstract data scientists from these burdensome tasks, enhancing development efficiency.
To further improve their MLOps process, the researchers plan to continue developing scalable and robust systems, evaluating their approach in production settings, such as chess tournaments. By integrating domain knowledge and MLOps practices, they aim to create more efficient and effective deep learning systems that can tackle complex problems in various domains.
In summary, the study demonstrates the importance of balancing quality attributes like accuracy, latency, and energy consumption when developing deep learning systems. By embedding domain knowledge and leveraging MLOps practices, researchers can create more efficient and effective systems that can drive innovation in various fields.
Cite this article: “Balancing Quality Attributes in Deep Learning System Development”, The Science Archive, 2025.
Deep Learning, Mlops, Edge Ai, Domain Knowledge, Energy Consumption, Resource Utilization, Accuracy, Latency, Automation, Data Processing Pipelines
