Last updated 11/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.56 GB | Duration: 2h 10m
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
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