How can you manage model versioning and governance in machine learning deployment?
Machine learning deployment is the process of making your trained models available for inference in production environments. It involves various challenges and best practices, such as scalability, reliability, security, and monitoring. However, one of the most critical aspects of ML deployment is model versioning and governance. This means keeping track of the changes and updates to your models, as well as ensuring that they comply with the business and regulatory requirements. In this article, you will learn how to manage model versioning and governance in machine learning deployment using some tools and techniques.