The Model Lifecycle as a Living Blueprint: A Cyberfun Workflow Comparison
This article explores the model lifecycle as a dynamic, living blueprint for machine learning operations. We compare three distinct workflow approaches: linear pipeline, iterative feedback loop, and agile sprint-based model development. Through detailed analysis, practical scenarios, and step-by-step guidance, we reveal how each workflow influences model performance, team collaboration, and deployment efficiency. Whether you are a data scientist, MLOps engineer, or project manager, this guide pr