  Predictive machine learning models nowadays are often updated in a stateless
and expensive way. The two main future trends for companies that want to build
machine learning-based applications and systems are real-time inference and
continual updating. Unfortunately, both trends require a mature infrastructure
that is hard and costly to realize on-premise. This paper defines a novel
software service and model delivery infrastructure termed Continual
Learning-as-a-Service (CLaaS) to address these issues. Specifically, it
embraces continual machine learning and continuous integration techniques. It
provides support for model updating and validation tools for data scientists
without an on-premise solution and in an efficient, stateful and easy-to-use
manner. Finally, this CL model service is easy to encapsulate in any machine
learning infrastructure or cloud system. This paper presents the design and
implementation of a CLaaS instantiation, called LiquidBrain, evaluated in two
real-world scenarios. The former is a robotic object recognition setting using
the CORe50 dataset while the latter is a named category and attribute
prediction using the DeepFashion-C dataset in the fashion domain. Our
preliminary results suggest the usability and efficiency of the Continual
Learning model services and the effectiveness of the solution in addressing
real-world use-cases regardless of where the computation happens in the
continuum Edge-Cloud.

Machine Learning;Artificial Intelligence;Distributed, Parallel, and Cluster Computing