Training an effective Machine learning (ML) model is an iterative process that requires effort
in multiple dimensions. Vertically, a single pipeline typically includes an initial ETL (Extract …

The digital revolution, rapidly decreasing storage cost, and remarkable results achieved by
state of the art machine learning (ML) methods are driving widespread adoption of ML …

Machine learning (ML) has become integral to fields like healthcare, finance, and
autonomous systems, but developing robust models requires significant computational …

In recent years, cloud computing has become an alternative to on-premise solutions for
enterprises to host their IT-stack. The main idea behind cloud computing is to offer remote …

Nowdays, it is prevalent to train deep learning (DL) models in cloud-native platforms that
actively leverage containerization and orchestration technologies for high elasticity, low and …

Recent advances in Machine Learning (ML) research have led to the creation of complex
models, trained with massive datasets [1, 2]. In order to excel at their tasks, state-of-the-art …

We conduct an empirical study of machine learning functionalities provided by major cloud
service providers, which we call machine learning clouds. Machine learning clouds hold the …

In cloud computing, serverless offerings like AWS Lambda offer notable benefits in
scalability and resource management. In theory, the flexibility and auto-scaling features of …

Recently, a plethora of applications have started to include a variety of Machine Learning
based inference systems for predictive analytics tasks across diverse domains such as …

The convergence behavior of many distributed machine learning (ML) algorithms can be
sensitive to the number of machines being used or to changes in the computing …