DataOps: Towards More Reliable Machine Learning Systems

As organizations increasingly rely on machine learning (ML) systems for mission-critical tasks, they face significant challenges in managing the raw material of these systems: data. Data scientists and engineers grapple with ensuring data quality, maintaining consistency across different versions, tracking changes over time, and coordinating work across teams. These challenges are amplified in defense contexts, where decisions based on ML models can have significant consequences and where strict regulatory requirements demand complete traceability and reproducibility. DataOps emerged as a response to these challenges, providing a systematic approach to data management that enables organizations to build and maintain reliable, trustworthy ML systems. In our previous post, we introduced our series on machine learning operations (MLOps) testing & evaluation (T&E) and outlined the three key domains we'll be exploring: DataOps, ModelOps and EdgeOps. . In this post, we're diving into DataOps, an area that focuses on the management and optimization of data throughout its lifecycle. DataOps is a critical component that forms the foundation of any successful ML system.