Machine learning, a blend of computer science and statistical analysis, involves using algorithms to make predictions based on data, and its effectiveness hinges on the quality and quantity of data available. The integration of machine learning into IoT devices has become widespread, but inefficiencies in data collection and processing present significant challenges, particularly for smaller companies. PubNub offers a solution by facilitating real-time data transmission and processing through its publish/subscribe model, reducing the costs associated with data extraction and feature processing. This model supports a scalable, pay-as-you-grow business approach and enhances security by ensuring message confidentiality, thereby lowering the need for expensive local machine learning hardware. By leveraging PubNub, companies can achieve more efficient, cost-effective, and secure machine learning operations within IoT networks.