What is MLOps governance and what are its key components?
MLOps refers to the discipline that combines ML, data science, and DevOps principles to manage the life cycle of ML models efficiently. The goal of MLOps is to create a streamlined pipeline for developing, deploying, and maintaining ML models, ensuring that these models provide reliable and consistent results. However, the implementation and management of such a practice require a governing framework to ensure adherence to best practices and standards. This governing framework is what we refer to as MLOps governance.
MLOps governance is an essential, yet often overlooked, aspect of implementing and managing ML models within an organization. It encapsulates a comprehensive set of rules, procedures, and guidelines aimed at overseeing the ML models throughout their life cycle. This governance plays a pivotal role in ensuring that the MLOps pipeline operates smoothly and ethically, mitigating any risks associated with ML model...
