Orchestrating ML workflows using Vertex AI Pipelines (managed Kubeflow pipelines)

ML solutions are complex and involve lots of steps, including data preparation, feature engineering, model selection, model training, testing, evaluation, and deployment. On top of these, it is really important to track and version control lots of aspects related to the ML model while in production. Vertex AI Pipelines on GCP lets us codify our ML workflows in such a way that they are easily composable, shareable, and reproducible. Vertex AI Pipelines can run Kubeflow as well as TensorFlow Extended (TFX)-based ML pipelines in a fully managed way. In this section, we will learn about developing Kubeflow pipelines for ML development as Vertex AI Pipelines.

Kubeflow is a Kubernetes-native solution that simplifies the orchestration of ML pipelines and makes experimentation easy and reproducible. Also, the pipelines are sharable. It comes with framework support for things such as execution monitoring, workflow...