The integration of Anomalib, an open-source deep learning library developed by Intel, with Comet, a comprehensive tool for managing and tracking machine learning experiments, represents a significant advancement in anomaly detection for Industry 4.0. Anomalib facilitates the benchmarking of various anomaly detection algorithms, focusing on image-based detection using unsupervised machine learning techniques, and supports models such as AutoEncoders and GANs. With its integration into Comet, users can efficiently manage experiment runs, optimize hyperparameters, and track model iterations, making the process of developing production-grade models more streamlined. Comet enhances this process by offering features like experiment tracking, model versioning, and performance monitoring, which are crucial for handling the iterative nature of machine learning in practical applications. This synergy allows for the deployment of robust anomaly detection systems in smart manufacturing environments, with tools to monitor and address model performance and data drift, thus ensuring the reliability and effectiveness of machine learning models in industrial settings.