Introduction to Kubeflow: Fundamentals

Introduction to Kubeflow: Fundamentals, available at Free, has an average rating of 4.17, with 8 lectures, based on 197 reviews, and has 3990 subscribers.

You will learn about Learn the basic Kubeflow architecture Learn what components make up the Kubeflow platform and how they work Learn what tools and add-ons are available to enhance the Kubeflow platform Learn how to install Kubeflow on AWS, GCP and locally This course is ideal for individuals who are Data scientists and DevOps with little or no experience with Kubeflow It is particularly useful for Data scientists and DevOps with little or no experience with Kubeflow.

Enroll now: Introduction to Kubeflow: Fundamentals

Summary

Title: Introduction to Kubeflow: Fundamentals

Price: Free

Average Rating: 4.17

Number of Lectures: 8

Number of Published Lectures: 8

Number of Curriculum Items: 8

Number of Published Curriculum Objects: 8

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Learn the basic Kubeflow architecture
Learn what components make up the Kubeflow platform and how they work
Learn what tools and add-ons are available to enhance the Kubeflow platform
Learn how to install Kubeflow on AWS, GCP and locally

Who Should Attend

Data scientists and DevOps with little or no experience with Kubeflow

Target Audiences

Data scientists and DevOps with little or no experience with Kubeflow

We’ll be covering the following Kubeflow topics in this course:

Architecture
Machine Learning Workflows
Components
Tools and Add-ons
Distributions
Kubeflow Community
Certification Prep

What is Kubeflow?

Kubeflow as a project got its start over at Google. The idea was to create a simpler way to run TensorFlow jobs on Kubernetes. So, Kubeflow was created as a way to run TensorFlow, based on a pipeline called TensorFlow Extended and then ultimately extended to support multiple architectures and multiple clouds so it could be used as a framework to run entire machine learning pipelines. The Kubeflow open source project (licensed Apache 2.0) was formally announced at the end of 2017.

In a nutshell, Kubeflow is the machine learning toolkit that runs on top of Kubernetes. Kubeflow’s combined components allow both data scientists and DevOps to manage data, train models, tune and serve them, as well as monitor them.

For whom is the “Introduction to Kubeflow” training and certification series of courses for?

Data scientists, machine learning developers, DevOps engineers and infrastructure operators who have little or no experience with Kubeflow and want to build their knowledge step-by-step, plus test their knowledge and earn certificates along the way.

What are the prerequisites for this course?

A basic understanding of cloud computing, Kubernetes and machine learning concepts is very helpful.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: The Basics

Lecture 1: Kubeflow Basics

Chapter 3: Machine Learning Workflows

Lecture 1: Machine Learning Workflows

Chapter 4: Kubeflow Components

Lecture 1: Kubeflow components overview

Chapter 5: Kubeflow Tools and Add-ons

Lecture 1: Kubeflow tools and add-ons

Chapter 6: Kubeflow Distributions

Lecture 1: Kubeflow Distributions

Chapter 7: Kubeflow Community

Lecture 1: Kubeflow Community

Chapter 8: Course Review

Lecture 1: Course Review

Instructors

Jimmy Guerrero
VP Developer Relations

Rating Distribution

1 stars: 2 votes
2 stars: 4 votes
3 stars: 31 votes
4 stars: 56 votes
5 stars: 104 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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