Integrating AI/ML Workloads with Serverless Cloud Computing: Optimizing Cost and Performance for Dynamic, Event-Driven Applications
Keywords:
Serverless cloud computing, artificial intelligence, machine learningAbstract
Event-driven applications may benefit from artificial intelligence, machine learning, serverless clouds. Serverless cloud computing might reduce performance and AI/ML workload costs. Real-time analytics, tailored solutions, and intelligent automation abound in IoT, e-commerce, and banking serverless AI/ML workloads. Serverless computing answers cost, complexity, and scalability as developers build code and application logic. Serverless artificial intelligence and machine learning workload difficulties include cold starts, stateful executions, memory and compute optimization, and real-time application low-latency solutions.
Scholars study. Container-based services protect data and enable long-running operations; asynchronous processing models and event-driven designs lower cold start latencies; serverless function memory and CPU resource tuning balance cost and performance. Function composition, data location optimization, and intelligent workload distribution help to offset serverless computing's financial effect on AI/ML workloads. Major cloud providers' cost comparison.
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