Kubeflow Fundamentals - How To Build Pipelines On The Cloud

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

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 environmentBuild and successfully deploy ML/AI Pipelines with KubeflowLearn the fundamentals of Kubernetes, GKE, Containers, and Clusters in relation to Machine LearningWork on a code lab with the GCP active cloud shellRun ML Pipelines and examine events and logs - GPU, CPU, and node management Create buckets, OAuth, and credentials with Google Cloud PlatformReview the basics of Kubeflow for AWS - EKSSet up scheduling and billing on GCP for project administration and managementCheck out deploying Jupiter notebook and for Kubeflow pipelinesAnd much more along the way! Course Set up and ToolsThis 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!

Overview

Section 1: What Are Containers & Virtual Machines - Introduction

Lecture 1 What Are Containers & Virtual Machines - 101 (Kubeflow)

Lecture 2 How Do Containers Work

Lecture 3 Isolation Differences Between Virtual Machines & Containers

Lecture 4 Modular Adaptability & Customization Of Containers

Lecture 5 Portability & Flexibility From VMs and Containers

Section 2: What Is Kubernetes - Fundamentals

Lecture 6 Quick Note - Kubernetes Section

Lecture 7 Introduction To Kubernetes & Container Deployment

Lecture 8 Tradition & Virtual Deployment Eras

Lecture 9 The Container Deployment Era & Benefits

Lecture 10 Kubernetes & Container Benefit Recap

Lecture 11 Why Use Kubernetes

Lecture 12 How is Kubernetes Useful

Lecture 13 Kubernetes Review

Section 3: Kubernetes & Clusters - Fundamentals

Lecture 14 What is a Kubernetes Cluster - Containers & Hosts

Lecture 15 Worker Nodes & The Master Node

Lecture 16 Kubernetes Microservice Application Example Part I

Lecture 17 Kubernetes Microservice Application Example Part II

Section 4: What Is Kubeflow - Introduction

Lecture 18 How Machine Learning Benefits From Kubernetes

Lecture 19 Kubeflow Beginning with TFX

Lecture 20 How Kubefow Makes It Easier For Developers

Lecture 21 How Kubeflow Works - Basics

Section 5: Kubelfow Project Set Up - Google Cloud Platform GCP

Lecture 22 Important Note - Codelab & GCP Billing

Lecture 23 Learn Kubeflow Lab Overview

Lecture 24 Set Up A Google Cloud Platform Project for The Kubeflow Example

Lecture 25 GCP GCloud Config Kubeflow Project Setup

Lecture 26 Create A Bucket For Kubeflow Example Storage

Lecture 27 Deploy A Kubeflow Pipeline - Kubernetes Engine Part I

Lecture 28 Deploy A Kubeflow Pipeline - Kubernetes Engine Part II

Lecture 29 Google Cloud Pipeline Billing And Budget Alerts

Lecture 30 Set Up GKE Cluseter

Lecture 31 Request GPU Quota Process

Lecture 32 Request GPU Quota Process - Once Approved

Section 6: Kubeflow Run A Pipeline From UI - Google Cloud Platform GCP

Lecture 33 Kubeflow Run A Pipeline From UI - Google Cloud Platform GCP

Lecture 34 Upload Yaml Pipeline Config Kubeflow File

Lecture 35 Input Parameters For Kubeflow Pipeline Run

Lecture 36 Kubeflow Pipeline Runs - Events & Logs

Lecture 37 Pipeline Deployment Checkpoint - Deprecated Example

Lecture 38 Iterative Recursive Example For Kubeflow - Pipeline Completion

Lecture 39 Quick Look at Kubeflow Teardown Command

Lecture 40 Optional - Deploying A Notebook WIth AI Platform GCP Kubeflow

Lecture 41 Optional - Kubeflow on AWS

Lecture 42 One Last Chance to Make This Course Better for Your Permanent Learning Library

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