Building End-to-end Machine Learning Workflows with Kubeflow 1

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Abhishek Kumar

3:30:22

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1. Course Overview

1. Course Overview.mp4

01:35

2. Introduction

1. Version Check.mp4

00:20

2. Course Introduction.mp4

05:03

3. Kubeflow Overview.mp4

04:19

4. Course Structure.mp4

02:50

5. Fashion-MNIST Use Case Overview.mp4

02:46

3. Setting up Kubeflow Environment

1. Introduction.mp4

01:00

2. Overview.mp4

01:12

3. Kubeflow Deployment Options.mp4

02:15

4. Setup Kubeflow on GCP.mp4

03:47

5. Demo - Setup Kubeflow Prerequisites.mp4

06:11

6. Demo - Setup OAuth for GCP Cloud IAP.mp4

03:19

7. Demo - Setup Kubeflow on GCP.mp4

10:11

8. Demo - Clean Kubeflow Environment on GCP.mp4

01:44

9. Summary.mp4

00:54

04. Exploring Kubeflow Components

01. Introduction.mp4

02:03

02. Overview.mp4

01:18

03. Docker Introduction.mp4

07:14

04. Demo - Docker Overview.mp4

04:03

05. Why Kubernetes.mp4

01:34

06. Kubernetes Introduction.mp4

02:27

07. Kubernetes Core Components.mp4

06:56

08. Demo - Kubernetes Overview (Part 1).mp4

05:27

09. Demo - Kubeflow Overview (Part 2).mp4

03:18

10. Demo - Kubeflow Central Dashboard Overview.mp4

01:32

11. Summary.mp4

01:49

05. Building Machine Learning Model on Kubeflow

01. Introduction.mp4

01:06

02. Overview.mp4

01:44

03. Model Development Process and Challenges.mp4

01:58

04. Kubeflow Components for Training.mp4

02:17

05. Fashion-MNIST Training Workflow.mp4

01:46

06. Kubeflow Notebook.mp4

01:01

07. Demo - Setting up Notebook Server with a Pre-built Image.mp4

04:48

08. Demo - Setting up Notebook Server with a Custom Image.mp4

05:36

09. Deep Learning Model Overview for Fashion-MNIST.mp4

03:55

10. Demo - Training in Kubeflow Notebook.mp4

08:13

11. Metadata Overview.mp4

01:00

12. Demo - Metadata Tracking.mp4

03:24

13. Kubeflow Fairing Overview.mp4

01:05

14. Demo - Kubeflow Fairing.mp4

04:15

15. Distributed Training.mp4

02:17

16. Demo - Distributed Training with GPU.mp4

03:46

17. Demo - Distributed Training with TFJob.mp4

07:59

18. Hyperparameter Tuning with Katib.mp4

02:14

19. Demo - Performing Hyperparameter Tuning with Katib.mp4

04:26

20. Summary.mp4

01:38

06. Serving Machine Learning Model on Kubeflow

01. Introduction.mp4

01:09

02. Overview.mp4

00:54

03. Model Serving Process and Challenges.mp4

03:27

04. Kubeflow Components for Serving.mp4

01:02

05. KFServing Overview.mp4

01:24

06. Demo - Serving Model Using KFServing.mp4

05:18

07. Demo - Pre and Post-processing Using KFServing.mp4

04:28

08. Canary Rollout Overview.mp4

01:28

09. Demo - Canary Rollout Using KFServing.mp4

03:20

10. Demo - Performance Monitoring Using KFServing, Prometheus, and.mp4

02:40

11. Demo - Auto Scaling and Load Testing.mp4

02:16

12. Summary.mp4

01:42

07. Build Machine Learning Pipeline Using Kubeflow Pipeline

01. Introduction.mp4

00:57

02. Overview.mp4

01:11

03. Machine Learning Workflow Pipeline and Challenges.mp4

02:58

04. Kubeflow Components for Building Pipeline.mp4

01:18

05. Kubeflow Pipeline Overview.mp4

02:19

06. Fashion-MNIST Use Case Pipeline.mp4

01:08

07. Demo - Building Kubeflow Pipeline with Hyperparam.mp4

06:06

08. Demo - Adding Training Step to Kubeflow Pipeline.mp4

03:05

09. Demo - Adding Serving Step to Kubeflow Pipeline.mp4

02:56

10. Demo - Building Kubeflow Pipeline from Notebook.mp4

03:21

11. Summary.mp4

01:46

8. Whats Next

1. Introduction.mp4

01:01

2. Extend Your Kubeflow Journey.mp4

03:33

Description

In this course, you will learn how to effectively build end-to-end scalable, production grade machine learning workflows using Kubeflow.

What You'll Learn?

Building production grade, scalable machine learning workflows is a complex and time-consuming task. In this course, Building End-to-end Machine Learning Workflows with Kubeflow 1, you will learn to use Kubeflow and discover how it can enable data scientists and machine learning engineers to build end-to-end machine learning workflows and perform rapid experimentation. First, you will delve into performing large scale distributed training. Next, you will explore hyperparameter tuning, model versioning, serverless model serving, and canary rollouts. Finally, you will learn how to build reproducible pipelines using various Kubeflow components, such as notebook server, fairing, metadata, katib, and Kubeflow pipelines. When you are finished with the course, you will be able to build end-to-end workflows for your machine learning and deep learning projects.

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Machine Learning

Abhishek Kumar

Instructor's Courses

Abhishek Kumar is a data science consultant, author, and Google Developers Expert (GDE) in machine learning. He holds a master’s degree from the University of California, Berkeley, and has been featured in the "Top 40 under 40 Data Scientist" list. He is also a public speaker and has delivered talks in top data conferences across the globe including Strata Data, AI conference, ODSC, and Fifth Elephant. His focus area is machine learning and deep learning at scale and is also a recipient of the "Hal Varian" award for his work on deep learning at UC Berkeley. Abhishek has worked on various machine learning and deep learning projects involving recommender systems, image recognition, forecasting, optimization, anomaly detection, and natural language processing.

Pluralsight

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Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.