Model Optimizations – Hyperparameter Tuning and NAS

We have now become quite familiar with some of the Vertex AI offerings related to managing data, training no-code and low-code models, and launching large-scale custom model training jobs (with metadata tracking and monitoring capabilities). As ML practitioners, we know that it is highly unlikely that the first model we train would be the best model for a given use case and dataset. Thus, in order to find the best model (which is the most accurate and least biased), we often use different model optimization techniques. Hyperparameter Tuning (HPT) and Neural Architecture Search (NAS) are two such model optimization techniques. In this chapter, we will learn how to configure and launch model optimization experiments using Vertex AI on Google Cloud.

In this chapter, we will first learn about the importance of model optimization techniques such as HPT and then learn how to quickly set up and launch HPT jobs within Google Vertex...