Summary

In this chapter, we discussed the importance of applying model optimization techniques to get the best performance for our application. We learned about two model optimization methods – HPT and NAS, with their similarities and differences. We also learned how to set up and launch large-scale HPT jobs on Vertex AI with code examples. Additionally, we discussed some best practices to get the best out of both HPT and NAS.

After reading this chapter, you should have a fair understanding of the term “model optimization” and its importance while developing ML applications. Additionally, you should now be confident about quickly setting up small to large-scale hyperparameter tuning experiments with the help of Vertex AI tooling on Google Cloud. You should also have a fair understanding of NAS, its differences from HPT, and the best practices for setting up a NAS job.

Now that we understand the importance and common methods of model optimization techniques...