LMDS is designed to be simple and easily replicated, with an emphasis on reducing technical overhead rather than computational overhead. Source code and architectural information is provided, allowing easy adoption by other research groups to share their ML models either on their local intranet or on a public facing website. Giving each team personal ownership of their work promotes diversity in the field, and allows each group to discover their own optimal workflow as well as share their findings to the wider community. The approaches outlined in this paper should allow the launch of new cloud platforms with minimal time and financial investment. In the following discussion we address considerations that must must be taken into account when sharing computational tools, and the role of such tools in the materials discovery workflow.