Model Deployment

Model deployment focuses on efficiently and reliably integrating machine learning models into real-world applications, addressing challenges like resource constraints, privacy concerns, and ensuring model stability and fairness. Current research emphasizes optimizing resource utilization through techniques such as microservice architectures, efficient model compression (e.g., using smaller, locally deployable LLMs or stitching together parts of larger models), and automated model training and deployment pipelines (MLOps). These advancements are crucial for making AI accessible across diverse environments and applications, improving cost-effectiveness, and mitigating risks associated with deploying complex models.