  Deep Learning has attracted considerable attention across multiple
application domains, including computer vision, signal processing and natural
language processing. Although quite a few single node deep learning frameworks
exist, such as tensorflow, pytorch and keras, we still lack a complete
processing structure that can accommodate large scale data processing, version
control, and deployment, all while staying agnostic of any specific single node
framework. To bridge this gap, this paper proposes a new, higher level
framework, i.e. Nemesyst, which uses databases along with model
sequentialisation to allow processes to be fed unique and transformed data at
the point of need. This facilitates near real-time application and makes models
available for further training or use at any node that has access to the
database simultaneously. Nemesyst is well suited as an application framework
for internet of things aggregated control systems, deploying deep learning
techniques to optimise individual machines in massive networks. To demonstrate
this framework, we adopted a case study in a novel domain; deploying deep
learning to optimise the high speed control of electrical power consumed by a
massive internet of things network of retail refrigeration systems in
proportion to load available on the UK National Grid (a demand side response).
The case study demonstrated for the first time in such a setting how deep
learning models, such as Recurrent Neural Networks (vanilla and Long-Short-Term
Memory) and Generative Adversarial Networks paired with Nemesyst, achieve
compelling performance, whilst still being malleable to future adjustments as
both the data and requirements inevitably change over time.

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