Cloud Platform Engineering for Enterprise AI and Machine Learning Workloads: Optimizing Resource Allocation and Performance
Keywords:
cloud platform engineering, enterprise AIAbstract
Corporate computing for artificial intelligence and machine learning calls for cloud platform architecture. Organizations needing computer resources, data processing, and resource distribution face problems as artificial intelligence and machine learning proliferate. This work maximizes cloud platform resource allocation and performance for business artificial intelligence and machine learning projects. For dynamic, resource-intensive artificial intelligence/machine learning applications, we assess hybrid cloud architectures, IaaS, and PaaS. Elastic resource management systems are used in this study to dynamically allocate computer resources depending on workload demands in order to minimize operational costs and resource underutilization.
We scale and deploy ML models using Kubernetes and Docker. By enabling microservices-based iterative AI application development, these systems increase modularity, version control, and teamwork. FaaS and serverless computing help to lower overhead for temporary training projects or inference assignments. We investigate various architectural approaches with respect to fault tolerance, latency, and throughput.
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