Cloud-Native AI: Challenges and Innovations in Deploying Large-Scale Machine Learning Models
Keywords:
Cloud, AI, Machine Learning, DeploymentAbstract
Large scale machine learning models are deployed with revolutionary speed in cloud native AI for the reasons of scalability, flexibility and cost efficiency. However, most of these challenges face a latency problem, resource management issue, and most of all security risk. This paper considers challenges in producing cloud native AI, such as containerization, microservices, and optimized inference pipelines and then it studies innovations in these areas. By qualitative and quantitative analysis, we examine best practices of deploying AI models effectively in cloud environment. The results indicate ways of cost effective and high-performance AI implementation, with a focus on automation, edge computation, and serverless hands in increasing the deployment of AI on a sustainable basis.
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