Training supervised deep learning models effectively
In Chapter 1, Deep Learning Life Cycle, it is emphasized that ML projects have a cyclical life cycle. In other words, a lot of iterative processes are carried out in the course of the project’s lifetime. To train supervised deep learning models effectively, there are a lot of general directions that should be taken based on different conditions, but the one that absolutely stands out across every problem is proper tooling. The tooling is more commonly known as ML operations (MLOps). Good MLOps systems for DL are easy to use and provide versioning methods for datasets and model experiments, visualization methods, easy ways to use DL libraries such as pytorch or keras with tensorflow, ease of deployment, ease of model comparisons using different metrics, ease of model tuning, good visualization of model training monitoring, and, finally, good feedback about the progress (this can be sent through messages and notifications for...
