Summary
In the chapter, we learned how to work with a Vertex AI-based managed training environment and launch custom training jobs. Launching custom training jobs on Vertex AI comes with a number of advantages, such as managed metadata tracking, no need to actively monitor jobs, and the ability to launch any number of experiments in parallel, choose your desired machine specifications to run your experiments, monitor training progress and results in near-real time fashion using the Cloud console UI, and run managed batch inference jobs on a saved model. It is also tighly integrated with other GCP products.
After reading this chapter, you should be able to develop and run custom deep learning models (using frameworks such as TensorFlow) on Vertex AI Workbench notebooks. Secondly, you should be able to launch long-running Vertex AI custom training jobs and also understand the advantages of the managed Vertex AI training framework. The managed Google Cloud console interface and TensorBoard...
