Metal Additive Manufacturing (AM) allows producing geometrically complex metal components, unlocking new design possibilities and making it suitable to sectors such as healthcare, automotive and aerospace. AM processes are complex and require the use of many sensors to extract relevant process information for its monitoring and control. In the last years, many studies have applied advanced Deep Learning methods to extract knowledge from AM processes. However, these developments are specific to a particular setup, problem or defectology. Furthermore, they lack frameworks and pipelines to guide throughout their development, and do not include AI-related tools for data labelling, visualization, and AI model development and deployment. With the aim of simplifying the development and deployment of AM process monitoring systems, a dashboard-based framework that makes use of AI for anomaly detection and for feature extraction is presented in this study. The framework helps with development and deployment of monitoring systems by easing the incorporation of new sensors and the extraction of new features from captured data by end users. In this study, a Laser Metal Deposition (LMD) process is considered as the use case to show the usefulness of the developed framework.