Developing Customizable Machine Learning Pipelines: A Systems-First Approach to Reliability and Trust

Machine learning models come and go, superseded by improved architectures, better data, or changing business requirements. Pipelines persist. They outlive individual models, span multiple projects, survive personnel changes, and adapt to shifting research questions. When treated as first-class systems rather than incidental scaffolding, pipelines transform from sources of fragility into foundations for sustainable AI capability.