  Deploying big-data Machine Learning (ML) services in a cloud environment
presents a challenge to the cloud vendor with respect to the cloud container
configuration sizing for any given customer use case. OracleLabs has developed
an automated framework that uses nested-loop Monte Carlo simulation to
autonomously scale any size customer ML use cases across the range of cloud
CPU-GPU "Shapes" (configurations of CPUs and/or GPUs in Cloud containers
available to end customers). Moreover, the OracleLabs and NVIDIA authors have
collaborated on a ML benchmark study which analyzes the compute cost and GPU
acceleration of any ML prognostic algorithm and assesses the reduction of
compute cost in a cloud container comprising conventional CPUs and NVIDIA GPUs.
