The final goal of all industrial machine learning (ML) projects is to develop ML products and
rapidly bring them into production. However, it is highly challenging to automate and …

Plastic pollution in water bodies is an unresolved environmental issue that damages all
aquatic environments, and causes economic and health problems. Accurate detection of …

Organizations are increasingly seeking to generate value and insights from their data by
integrating advances in artificial intelligence (AI)(eg, machine learning (ML) systems) into …

This paper is an concentrated overview of the Machine Learning Operations (MLOps) area.
Our aim is to define the operation and the components of such systems by highlighting the …

Over the past few decades, the substantial growth in enterprise-data availability and the
advancements in Artificial Intelligence (AI) have allowed companies to solve real-world …

Recently, Machine Learning (ML) has become a widely accepted method for significant
progress that is rapidly evolving. Since it employs computational methods to teach machines …

Machine Learning (ML) has become a fast-growing, trending approach in solution
development in practice. Deep Learning (DL) which is a subset of ML, learns using deep …

MLOps is essential to streamline the machine learning (ML) development process, ensure
ML models stay operational, and provide users with the desired value. MLOps enhances the …

Artificial intelligence systems are increasingly being used in industrial applications, security
and military contexts, disaster response complexes, policing and justice practices, finance …

Software Defect Prediction (SDP) is an integral aspect of the Software Development Life-
Cycle (SDLC). As the prevalence of software systems increases and becomes more …