Summary

In this chapter, we explored the three fundamental pillars of model governance for deep learning models: model utilization, model monitoring, and model maintenance. Model utilization ensures the effective, efficient, ethical, and responsible utilization of deep learning models, while model monitoring allows for ongoing evaluation of performance, identification of potential bias or drift, and infrastructure-related metrics. Model maintenance, on the other hand, focuses on regular updates and refinements to keep models aligned with evolving data landscapes and business requirements.

We also dove into and learned about the technical steps for monitoring deep learning models using NVIDIA Triton Server, Prometheus, and Grafana. By diligently considering the components for model governance, deep learning architects can effectively manage the challenges posed by these complex models in production and consistently harness their potential for driving valuable insights and decisions...