Machine learning teams are increasingly using MLOps platforms like Comet to manage the lifecycle of their models, which includes storing, monitoring for drift, re-training, and deploying models to production. Comet's Model Registry and Production Monitoring solutions provide comprehensive tools for tracking model training lineage, analyzing predictions, and detecting failures, thereby facilitating the re-training process with updated datasets. The platform supports Model CI/CD workflows by allowing the automation of model promotion through Webhooks and enforcing model status changes with an approval process managed by Workspace Admins. This ensures that updates, which can significantly impact operations like autonomous driving or fraud detection, are carefully controlled and audited. As organizations deploy more models, integrating a robust CI/CD process with platforms like Comet becomes essential for maintaining model performance and reliability.