  Machine Learning operations is unarguably a very important and also one of
the hottest topics in Artificial Intelligence lately. Being able to define very
clear hypotheses for actual real-life problems that can be addressed by machine
learning models, collecting and curating large amounts of data for model
training and validation followed by model architecture search and actual
optimization and finally presenting the results fits very well the scenario of
Data Science experiments. This approach however does not supply the needed
procedures and pipelines for the actual deployment of machine learning
capabilities in real production grade systems. Automating live configuration
mechanisms, on the fly adapting to live or offline data capture and
consumption, serving multiple models in parallel either on edge or cloud
architectures, addressing specific limitations of GPU memory or compute power,
post-processing inference or prediction results and serving those either as
APIs or with IoT based communication stacks in the same end-to-end pipeline are
the real challenges that we try to address in this particular paper. In this
paper we present a unified deployment pipeline and freedom-to-operate approach
that supports all above requirements while using basic cross-platform tensor
framework and script language engines.
