Abstract

The rapid growth of Artificial Intelligence (AI) has sparked the demand for scalable, efficient, and cost-effective deployment solutions. In particular, these methods are crucial for handling the increasing computing demand and complexity of AI models in human-centric tasks like real-time picture classification, speech recognition, and natural language processing. The three main topics of this paper's exploration of scalable AI deployment methodologies are real-time inference, model serving, and distributed training. Optimized deployment pipelines, parallel processing, and cloud infrastructure are essential for striking a balance between performance and cost. This study offers a thorough analysis of various technologies, looking at their cost-effectiveness, suitability for use in real-world settings, and capacity to handle huge datasets. Along with evaluations, the article provides a comparative study of various approaches based on cost, efficiency, and scalability parameters. Tables are used to highlight the differences between the approaches. A survey of pertinent literature covering the years 2003 to 2022 gives context for the advancement of AI deployment technology