The success of a fully realised Industry4.0 lies in the democratisation of intelligence and the capacity for Industrial "Things" to autonomously act based on the knowledge they have. Effectively, turning each and every factory into a computer that is made up of modular processes within, in the form of Cyber-Physical systems. And central to that success, is the ease with which Industrial things like pumps and sensors can be embedded with Machine Learning functionality. To learn more about Embedded ML, I had a chat with Zin Thein Kyaw who is a Sr Success Engineer at Edge Impulse, a company on a mission to enable the ultimate development experience for machine learning on embedded devices for sensors, audio, and computer vision, at scale.  You can check out our conversation at the link below Outline: ✔️ Integrating ML into industrial machines and sensors ✔️ Benefits of ML at the Edge of IIoT Network ✔️ Current applications of Embedded ML in industrial assets ✔️ Choosing an Embedded Processor for ML ✔️ Workflow for developing and deploying Embedded ML models  ✔️ Integration of Edge Impulse with Tensorflow and Resource Optimisation ✔️ Industrial Data Collection and Data Availability ✔️ Application of Deep Learning in Industrial Systems ✔️ The Future of Embedded ML  ✔️ The Edge Impulse Ecosystem & Developer Resources