Cloud platforms are excellent suppliment to existing communications methods, and need not be a prohibitively expensive to deploy. While ML models may be costly to train and require significant compute resources, after training is performed the models can often be deployed using lower end hardware and still make inferences in a timely manner. Consequently, the LMDS platform with the architecture outlined above may be deployed on relatively cheap hardware. We provide a minimum framework for launch of platforms (fig. ), as well as how we expand this for isolation of applications (fig. ). The frameworks and implementation details provided here should provide a reasonable starting point for other researchers to share their tools with the wider community.