Kubeflow Fundamentals – How To Build ML/AI Pipelines

Kubeflow Fundamentals - How To Build ML/AI Pipelines

Kubeflow Fundamentals – How To Build ML/AI Pipelines

Learn Kubeflow by Example with Machine Learning – Deploy ML AI Pipelines on Google Cloud Platform – Kubernetes & AWS

Language: english

Note: 3.2/5 (105 notes) 10,218 students

Instructor(s): Clarian North

Last update: 2021-10-27

What you’ll learn

  • How to build ml/ai pipelines with Kubeflow from scratch
  • Deploy Kubeflow on GCP and AWS with real-world examples, and best practices
  • Kubernetes & Kubeflow fundamentals
  • Run multiple ML pipelines with the Kubeflow UI

 

Requirements

  • No programming experience needed. You will learn everything you need to know in the course.

 

Description

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.


What you will learn in this course:

  • Setting up the Google Cloud Platform development environment

  • Build and successfully deploy ML/AI Pipelines with Kubeflow

  • Learn the fundamentals of Kubernetes, GKE, Containers, and Clusters in relation to Machine Learning

  • Work on a code lab with the GCP active cloud shell

  • Run ML Pipelines and examine events and logs – GPU, CPU, and node management

  • Create buckets, OAuth, and credentials with Google Cloud Platform

  • Review the basics of Kubeflow for AWSEKS

  • Set up scheduling and billing on GCP for project administration and management

  • Check out deploying Jupiter notebook and for Kubeflow pipelines

  • And much more along the way! 

Course Set up and Tools

This course develops its Kuebflow project and source code with Active Cloud Shell on the Google Cloud Platform – it’s free to set up, but deploying and running the pipelines to completion yourself will require you to activate a billing account and it’s important that you monitor your costs in that case (this is optional and we explain the steps and procedure if you’re interested in spending a bit more to see kubeflow machine learning pipelines in action).

Is this the right course for you?

This course is straight to the point, time-sensitive, and focuses on completing the project at hand (the reasons and explanations for the code and how it works) as the primary. Besides the initial sections which is meant for a 101 introduction into the basics of Kubeflow and Kubernetes for all levels, pretty much all of this course after that is just building out our Kubeflow Pipeline stopping to explain the techniques and dependencies connections along the way. If you are the type of person who gets the most out of learning ‘by doing’, then this course will be for you.

I’m looking forward to discovering the value and real ease of what it means to make our lives much more simple and efficient thanks to what kubeflow can offer!

And whenever you’re ready,  I’ll see you in the lessons!

 

Who this course is for

  • Data scientists interested in learning the fundamentals of Kubeflow
  • Technologists interested in learning the fundamentals of Kubeflow
  • ML Engineers interested in a hands-on tutorial for Kubeflow
  • Data Engineers interested in a hands-on tutorial for Kubeflow

 

Course content

  • What Are Containers & Virtual Machines – Introduction
    • What Are Containers & Virtual Machines – 101 (Kubeflow)
    • How Do Containers Work
    • Isolation Differences Between Virtual Machines & Containers
    • Modular Adaptability & Customization Of Containers
    • Portability & Flexibility From VMs and Containers
  • What Is Kubernetes – Fundamentals
    • Quick Note – Kubernetes Section
    • Introduction To Kubernetes & Container Deployment
    • Tradition & Virtual Deployment Eras
    • The Container Deployment Era & Benefits
    • Kubernetes & Container Benefit Recap
    • Why Use Kubernetes
    • How is Kubernetes Useful
    • Kubernetes Review
  • Kubernetes & Clusters – Fundamentals
    • What is a Kubernetes Cluster – Containers & Hosts
    • Worker Nodes & The Master Node
    • Kubernetes Microservice Application Example Part I
    • Kubernetes Microservice Application Example Part II
  • What Is Kubeflow – Introduction
    • How Machine Learning Benefits From Kubernetes
    • Kubeflow Beginning with TFX
    • How Kubefow Makes It Easier For Developers
    • How Kubeflow Works – Basics
  • Kubelfow Project Set Up – Google Cloud Platform GCP
    • Important Note – Codelab & GCP Billing
    • Learn Kubeflow Lab Overview
    • Set Up A Google Cloud Platform Project for The Kubeflow Example
    • GCP GCloud Config Kubeflow Project Setup
    • Create A Bucket For Kubeflow Example Storage
    • Deploy A Kubeflow Pipeline – Kubernetes Engine Part I
    • Deploy A Kubeflow Pipeline – Kubernetes Engine Part II
    • Google Cloud Pipeline Billing And Budget Alerts
    • Set Up GKE Cluseter
    • Request GPU Quota Process
    • Request GPU Quota Process – Once Approved
  • Kubeflow Run A Pipeline From UI – Google Cloud Platform GCP
    • Kubeflow Run A Pipeline From UI – Google Cloud Platform GCP
    • Upload Yaml Pipeline Config Kubeflow File
    • Input Parameters For Kubeflow Pipeline Run
    • Kubeflow Pipeline Runs – Events & Logs
    • Pipeline Deployment Checkpoint – Deprecated Example
    • Iterative Recursive Example For Kubeflow – Pipeline Completion
    • Quick Look at Kubeflow Teardown Command
    • Optional – Deploying A Notebook WIth AI Platform GCP Kubeflow
    • Optional – Kubeflow on AWS
    • One Last Chance to Make This Course Better for Your Permanent Learning Library

 

Kubeflow Fundamentals - How To Build ML/AI Pipelines

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