Kubeflow Fundamentals – How To Build ML/AI Pipelines

In this course, we will cover all the fundamentals first of Kubeflow with slides and presentations and then build and deploy ML/AI Pipelines with Kubeflow together using the Google Cloud Platform (GCP) along with the GKE and active cloud shell. We will also learn the fundamentals of Kubernetes and Kubeflow along with GCP project management as we move forward together with the code lab.

Get hands–on experience early with an exciting technology making ML deployments much easier thanks to the power of Kubeflow!

This is the course you’ve been looking for to get a clear and concise explanation of what is Kubeflow and the value it presents for creating efficiency with Machine Learning.

If you’d like to quickly and simply go through each step of code together and discuss the conventions and the commands for setting up cloud–native and run multiple pipelines together – we’re even going to take a look at a recursive tutorial that runs iterative prediction calculations with increasing margins of acceptable results, then this is perfect course is for you!

This course is modular and intended to be beginner–friendly as well, so that if you are coming from a less technical or more business–minded side or you are just keen on reviewing the fundamentals of Kubernetes and, VMS, containers and clusters and how they have significant value in relation to deploying and running machine learning pipelines then you will also find clear, simplified and contextualized examples as part of this course as well. Just remember, those sections are purely optional and if you already have fundamental knowledge please feel free to skip directly to the code lab and get started hands–on with me.