Manual Model Tracking
Manual Model Tracking is a practice in machine learning and data science where developers manually log, version, and document the details of their machine learning models, experiments, and datasets without relying on automated tools. This involves recording parameters, metrics, code versions, and data sources in spreadsheets, notebooks, or text files to ensure reproducibility and traceability. It is a foundational approach for managing the lifecycle of models in environments where automated MLOps tools are not available or feasible.
Developers should use Manual Model Tracking when working in small-scale projects, research settings, or early prototyping phases where setting up automated MLOps infrastructure is overkill or resource-intensive. It is crucial for maintaining reproducibility in academic papers, debugging model performance issues, and collaborating in teams without dedicated DevOps support. For example, in a startup with limited resources, manually tracking model iterations in a shared Google Sheet can prevent confusion and ensure that the best-performing model is deployed.