Scientific computation problems have been faced with the
need to analyze increasing amounts of data as part of their
application workflows, and the science-based model is being
combined with big data and machine learning models to
solve complex problems and phenomena [1][2]. The machine
learning workflow is composed of some reproducible steps
that can be executed as a pipeline to build a model efficiently
by saving iteration time, helping in debugging and detecting
[3]. Currently, businesses and researchers are investigating
and improving the methodology of developing and deploying
machine learning workflows in both training and inference
phases, which helps the data science team focus on their
requirements and the data engineer team deploy and operate
machine learning workflows efficiently and automatically [4].
This work presents an architecture for automatic machine
learning workflows, which provides capabilities of monitoring
and automatic management on the end-to-end life-cycle of
machine learning workflows, including tracking and observing
at the training stage, and releasing, monitoring, deployment,
auto-detecting and infrastructure management at the inference
stage. To validate feasibility, we have conducted a case study
based on our architecture and deployed it in the cloud, and
showed its automation
