In the era of the fourth industrial revolution, several concepts have arisen in parallel with this
new revolution, such as predictive maintenance, which today plays a key role in sustainable …

Machine learning (ML) has succeeded in improving our daily routines by enabling
automation and improved decision making in a variety of industries such as healthcare …

Model efficiency is a critical aspect of developing and deploying machine learning models.
Inference time and latency directly affect the user experience, and some applications have …

Multimodal target/aspect sentiment classification combines multimodal sentiment analysis
and aspect/target sentiment classification. The goal of the task is to combine vision and …

The development and deployment of machine learning systems can be executed easily with
modern tools, but the process is typically rushed and means-to-an-end. Lack of diligence …

Organizations rely on machine learning engineers (MLEs) to operationalize ML, ie, deploy
and maintain ML pipelines in production. The process of operationalizing ML, or MLOps …

Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …

Digitization in healthcare systems, with the wid adoption of Electronic Health Records,
connected medical devices, software and systems providing efficient healthcare service …

Companies struggle to continuously develop and deploy Artificial Intelligence (AI) models to
complex production systems due to AI characteristics while assuring quality. To ease the …

Deep learning is a sub-branch of artificial intelligence that acquires knowledge by training a
neural network. It has many applications in the field of banking, automobile industry …