Internet of Things (IoT) devices (e.g., sensors and actuators) are being actively deployed at scale to automate and control many aspects of physical environments. While models (e.g., ranging from black box to white box approaches) can be used to support the actuation of such devices, as the scale of these instrumentations grows, the complexity of the required model increases. Consequently, controlling and coordinating IoT devices at a massive scale to achieve a common goal (e.g., flatten demand) or individual goals (e.g., minimize cost) becomes a challenge. The goal of this talk is to start a discussion about possible research directions and collaborations between BETS and RISE in the context of ML and IoT. In particular, we will present a brief overview of our XBOS-DR project, which aims to react to Demand Response (DR) events in the grid by controlling smart buildings. We will also present three possible research avenues: (1) Using Reinforcement Learning techniques (single and multi-agent) for the control of smart buildings to react to DR events, (2) Using meta-data to drive ML pipeline composition, and (3) Using CLIPPER to serve XBOS-DR predictions securely over WAVE.