From the course: Leveraging Cloud-Based Machine Learning on Google Cloud Platform: Real World Applications
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Execution - Google Cloud Tutorial
From the course: Leveraging Cloud-Based Machine Learning on Google Cloud Platform: Real World Applications
Execution
- [Instructor] So now let's talk about Kubeflow deployment or Kubeflow execution. And again, we're dealing with a few core concepts that you need to understand before you can push a Kubeflow application into production. So you have to understand the concept of a pipeline. It's everything with Kubeflow. Your ability to define visually how a pipeline works and how to link things together, objects, triggers, events, and then deal with what a component is. In other words, a core end point of a pipeline that actually carries out an activity. The ability to deal with graphs and ultimately, run these as experiments. Keep in mind that an experiment is an instance of machine learning and we're learning from that instance. And so that's why we call them experiments because they're ongoing. They're not something that we build and deploy and forget about. It's something that we continuously improve. When you execute Kubeflow, you're…
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