Summary
In this chapter, we have learned about two popular ML workflow orchestration tools – Vertex AI Pipelines (managed Kubeflow) and Cloud Composer (managed Airflow). We have also implemented a Vertex Pipeline for an example use case, and similarly, we have also developed and executed an example DAG with Cloud Composer. Both Vertex AI Pipelines and Cloud Composer are managed services on GCP and make it really easy to set up and launch complex ML and data-related workflows. Finally, we have learned about getting online and batch predictions on Vertex AI for our custom models, including some best practices related to model deployments.
After reading this chapter, you should have a good understanding of different ways of carrying out ML workflow orchestration on GCP and their similarities and differences. Now, you should be able to write your own ML workflows and orchestrate them on GCP via either Vertex AI Pipelines or Cloud Composer. Finally, you should also be confident...
