DNN-Powered MLOps Pipeline Optimization for Large Language Models: A Framework for Automated Deployment and Resource Management

Background Large language models (LLMs) have experienced exponential growth in size and complexity, creating unprecedented challenges in deployment and operational management. Traditional MLOps approaches struggle with efficiently managing the scale, resource requirements, and dynamic nature of these models, highlighting the need for more sophisticated management solutions. Objective To develop and validate a novel framework leveraging deep neural networks (DNNs) for optimizing MLOps pipelines specifically for LLMs, focusing on automating deployment decisions, resource allocation, and pipeline optimization while maintaining performance and cost efficiency. Methods We implemented a multi-stream neural architecture for processing heterogeneous operational metrics, coupled with an adaptive resource allocation system and sophisticated deployment orchestration mechanism. The framework was evaluated across multiple cloud environments using production workloads from various organizations, testing its performance in multi-cloud environments, high-throughput production systems, and cost-sensitive deployments. Results Our framework demonstrated significant improvements over traditional MLOps approaches, achieving a 20% enhancement in resource utilization, 15% reduction in deployment latency, and 16% decrease in operational costs. The system showed consistent performance across various deployment scenarios, with rapid adaptation to changing workload patterns and efficient resource allocation. These improvements were validated through extensive testing across different deployment scenarios and workload patterns, demonstrating the framework’s ability to maintain performance stability while optimizing resource usage. The system’s adaptive capabilities proved particularly effective in handling varying workload intensities, automatically adjusting resource allocation and deployment configurations to maintain optimal performance while minimizing operational costs. Conclusion The proposed DNN-powered MLOps framework represents a significant advancement in automated management of large-scale language models. The system’s ability to adapt to varying workloads and automatically optimize deployment strategies provides a robust solution for modern LLM deployment challenges, offering improved operational efficiency and cost management while maintaining system reliability.