Governing a deep learning model through maintenance

Metrics logging, dashboard building, logged metrics analysis, and alerts are essential components of model monitoring, but they are only effective when followed by appropriate actions, which are covered under model maintenance. Model maintenance is akin to a skilled pit crew in a car race, regularly fine-tuning and optimizing the performance of deep learning models to keep them running efficiently and effectively. Like how a pit crew conducts rapid repairs, refuels, and adjusts the car’s components to adapt to changing race conditions, model maintenance involves updating the models to account for environmental changes, improving and refining the models with new data obtained from feedback loops, and performing incident responses on miscellaneous issues. This ensures that the models consistently stay on track, deliver valuable insights, and drive informed decision-making in the ever-evolving landscape of data and business requirements...