MLOps: Version Control

In today’s rapidly evolving business landscape, the effective management of technology and data has become critical for organizations to stay competitive and achieve sustainable growth. Two key disciplines that have emerged to address these challenges are DevOps and MLOps (Machine Learning Operations). Both DevOps and MLOps play pivotal roles in enabling businesses to optimize their software development and machine learning processes, respectively, to drive innovation, enhance efficiency, and deliver value to customers.

Version control, using tools such as Git, is a vital component of DevOps and MLOps. It is imperative to maintain version control not only for software in DevOps but also for machine learning models in MLOps. Equally important, yet often overlooked, is the application of robust version control to feature engineering, a vital component of MLOps.

In feature management, version control is crucial for a number of reasons. Version control prevents the introduction of data pipeline errors via proper peer review. Secondly, machine learning models are intricately linked to the features they were trained on. Moreover, deprecated feature versions may be utilized by multiple ML models in production, necessitating effective management of these interdependencies. Lastly, without role-based access control, an AI data pipeline becomes susceptible to attacks from malicious actors, emphasizing the need for version control.

To implement version control effectively, an integrated solution is necessary. Firstly, a structured process involving requests and documentation is required to identify proposed changes in feature definitions, along with a clear rationale behind these changes. Secondly, review and approval processes must be established, incorporating an audit trail to provide a governance layer for effective management.

Authorization through role-based access control (RBAC) is another vital component, ensuring that only authorized individuals have the ability to put a feature into production. Additionally, a reliable source of truth for the production version of a feature should be established, which can be achieved by implementing a self-organizing feature catalog. Moreover, guardrails should be put in place to automatically identify production deployment dependencies when a feature version changes. These guardrails act as a governance layer, allowing potential impacts to be managed effectively. Lastly, a central management dashboard should be implemented to monitor feature usage and provide alerts when deprecated feature versions continue to be used.

A mere feature store is insufficient to achieve comprehensive version control. It is imperative to tightly integrate the feature store with a feature catalog and a governance layer. By applying MLOps principles to feature engineering and AI data pipelines, organizations can achieve reproducibility, scalability, and governance in their machine learning initiatives. 

Follow this link to learn more about the components of an integrated feature engineering and management platform. https://featurebyte.com/product