Speaker
Description
Ensuring efficient use of resources and longevity of machine learning projects requires careful consideration of the full machine learning lifecycle especially when models are deployed to interact with live control systems or end users. We present Lume Deployment a framework of standardised modules built for rapid development and deployment of machine learning models and their integration to the control system. The framework is built around a modular approach which separates the system, the data and the model from each other via well-defined interfaces, which also allows easy expandability of the framework. We showcase several case studies and use-cases that demonstrate the framework’s flexibility in various real-world scenarios. Additionally, we outline current developments and possible future developments focusing on the framework’s stability and automation model evaluation tasks.
