  This paper investigates a paradigm for offering artificial intelligence as a
service (AI-aaS) on software-defined infrastructures (SDIs). The increasing
complexity of networking and computing infrastructures is already driving the
introduction of automation in networking and cloud computing management
systems. Here we consider how these automation mechanisms can be leveraged to
offer AI-aaS. Use cases for AI-aaS are easily found in addressing smart
applications in sectors such as transportation, manufacturing, energy, water,
air quality, and emissions. We propose an architectural scheme based on SDIs
where each AI-aaS application is comprised of a monitoring, analysis, policy,
execution plus knowledge (MAPE-K) loop (MKL). Each application is composed as
one or more specific service chains embedded in SDI, some of which will include
a Machine Learning (ML) pipeline. Our model includes a new training plane and
an AI-aaS plane to deal with the model-development and operational phases of AI
applications. We also consider the role of an ML/MKL sandbox in ensuring
coherency and consistency in the operation of multiple parallel MKL loops. We
present experimental measurement results for three AI-aaS applications deployed
on the SAVI testbed: 1. Compressing monitored data in SDI using autoencoders;
2. Traffic monitoring to allocate CPUs resources to VNFs; and 3. Highway
segment classification in smart transportation.
