Machine Learning Operations (MLOps) is an approach to managing the entire lifecycle of a
machine learning model. It has evolved over the last years and has started attracting many …

Serverless computing, or Function-as-a-Service (FaaS), enables a new way of building and
scaling applications by allowing users to deploy fine-grained functions while providing fully …

The traditional cloud-centric approach for Deep Learning (DL) requires training data to be
collected and processed at a central server which is often challenging in privacy-sensitive …

Serverless computing is gaining popularity for machine learning (ML) serving workload due
to its autonomous resource scaling, easy to use and pay-per-use cost model. Existing …

Function-as-a-Service (FaaS) serverless computing enables a simple programming model
with almost unbounded elasticity. Unfortunately, current FaaS platforms achieve this …

The serverless model represents a paradigm shift in the cloud: as opposed to traditional
cloud computing service models, serverless customers do not reserve hardware resources …

Although serverless computing is a popular paradigm, current serverless environments have
high overheads. Recently, it has been shown that serverless workloads frequently exhibit …

Serverless computing has gained significant popularity due to its scalability, cost-
effectiveness, and ease of deployment. With the exponential growth of data, organizations …

MicroVM snapshotting significantly reduces the cold start overheads in serverless
applications. Snapshotting enables storing part of the physical memory of a microVM guest …

In recent years, serverless computing has garnered significant attention owing to its high
scalability, pay-as-you-go billing model, and efficient resource management provided by …