Deployed AI platforms typically ship with bulky system architectures which present
bottlenecks and a high risk of failure. A serverless deployment can mitigate these factors and …

We propose a set of optimization techniques for transforming a generic AI codebase so that
it can be successfully deployed to a restricted serverless environment, without compromising …

Serverless computing has gained a strong traction in the cloud computing community in
recent years. Among the many benefits of this novel computing model, the rapid auto-scaling …

Although serverless computing generally involves executing short-lived “functions,” the
increasing migration to this computing paradigm requires careful consideration of energy …

Serving machine learning inference workloads on the cloud is still a challenging task on the
production level. Optimal configuration of the inference workload to meet SLA requirements …

Machine learning (ML) is an important part of modern data science applications. Data
scientists today have to manage the end-to-end ML life cycle that includes both model …

The salient pay-per-use nature of serverless computing has driven its continuous
penetration as an alternative computing paradigm for various workloads. Yet, challenges …

Serverless computing has sparked a massive interest in both the cloud service providers
and their clientele in recent years. This model entails the shift of the entire matter of resource …

Serverless computing is a cloud-based execution paradigm that allows provisioning
resources on-demand, freeing developers from infrastructure management and operational …

Serverless computing platforms provide Function (s)-as-a-Service (FaaS) to end users while
promising reduced hosting costs, high availability, fault tolerance, and dynamic elasticity for …