From the beginning of the book until this chapter, we proceeded step by step and covered many important topics. In Chapter 6, we talked deeply about automating machine learning (ML) pipelines. In this chapter, we will discuss the best practices and strategies for updating and managing ML models in production environments. We start with an investigation of model versioning, which is essential for traceability and consistency. Then, we discuss best practices, including metadata storage, semantic versioning, and use of model registries, with tools like DVC, MLflow, and Kubeflow. Real-world examples show how to implement versioning efficiently.