With the advances in machine learning (ML) and deep learning (DL) techniques, and the
potency of cloud computing in offering services efficiently and cost-effectively, Machine
Learning as a Service (MLaaS) cloud platforms have become popular. In addition, there is
increasing adoption of third-party cloud services for outsourcing training of DL models,
which requires substantial costly computational resources (eg, high-performance graphics
processing units (GPUs)). Such widespread usage of cloud-hosted ML/DL services opens a …