KubeFlow Bootcamp

Focused View

Jose Portilla

7:21:26

52 View

1 - Course Introduction

1 - FAQ AND COURSE DOWNLOADS.html

2 - Course Overview.mp4

02:51

2 - Google Cloud Overview and Setup

3 - Overview of Google Cloud Section.mp4

01:36

4 - What is Cloud A Cloud Computing Overview.mp4

12:02

5 - GCP Network Infrastructure.mp4

12:19

6 - GCP Network Connections.mp4

18:58

7 - Why Choose GCP.mp4

12:37

8 - Google Cloud Account SetUp.mp4

06:59

9 - Billing and Budgets.mp4

04:41

10 - Billing Tour DEMO.mp4

07:05

11 - Setting a Budget Alert DEMO.mp4

04:16

12 - Google Cloud Storage Options.mp4

05:39

13 - Cloud Storage Overview.mp4

23:16

14 - Cloud Storage DEMO.mp4

16:45

3 - Kubernetes Overview

15 - Introduction to Kubernetes Section.mp4

01:17

16 - Kubernetes Basics Overview.mp4

07:37

17 - Understanding Containers.mp4

09:07

18 - Understanding Nodes and Control Plane.mp4

04:26

19 - Understanding Kubernetes API.mp4

06:47

20 - Container Images.mp4

06:47

21 - Cloud Build DEMO.mp4

15:49

22 - Google Kubernetes Engine GKE.mp4

04:13

23 - Kubernetes Architecture.mp4

15:44

24 - GKE via GUI DEMO.mp4

05:33

25 - Kubectl.mp4

05:18

26 - GKE via Command Line DEMO.mp4

19:24

27 - Kubernetes Deployments.mp4

07:56

28 - Kubernetes Deployment Updates.mp4

03:28

29 - Kubernetes Deployment Strategies.mp4

08:12

30 - Kubernetes Deployment DEMO.mp4

12:07

31 - Kubernetes Pod Networking.mp4

04:59

32 - Kubernetes Storage with Volumes.mp4

07:29

33 - Volume Storage DEMO.mp4

12:49

4 - Understanding Machine Learning Fundamentals

34 - Introduction to Machine Learning Fundamentals.mp4

01:48

35 - Machine Learning Pathway.mp4

10:16

36 - Why Machine Learning.mp4

09:15

37 - Types of Machine Learning Algorithms.mp4

07:47

38 - Supervised Learning Process.mp4

13:41

39 - AI vs ML.mp4

05:24

40 - AI and ML on Google Cloud.mp4

08:29

41 - Vertex AI Overview.mp4

05:38

42 - Vertex AI Workbench Notebooks in the Cloud.mp4

08:55

5 - Kubeflow Pipelines with Google Cloud

43 - Kubeflow Section Overview.mp4

06:14

44 - Vertex AI Pipelines.mp4

13:02

45 - Vertex AI Pipelines and SDKs.mp4

05:08

46 - AI Pipeline via Notebook DEMO.mp4

25:28

46 - vertex-ai-emoji-pipeline.zip

47 - Kubeflow Overview.mp4

06:15

48 - Kubeflow DSL.mp4

06:41

49 - Kubeflow PreBuilt Components.mp4

03:42

50 - Kubeflow Python Components.mp4

05:01

51 - Kubeflow Custom Components.mp4

05:51

52 - Kubeflow Compilation.mp4

04:45

Description

Learn how to use Kubeflow for Machine Learning at scale on Google Cloud!

What You'll Learn?

Understand the core concepts of Kubeflow and its role in building scalable and portable machine learning workflows on Google Cloud.
Learn to deploy and manage Kubeflow pipelines to automate end-to-end machine learning workflows on Google Cloud.
Gain proficiency in utilizing Kubeflow components
Develop skills in leveraging Google Cloud's AI Platform for training and serving machine learning models within the Kubeflow environment.
Explore the integration of Kubeflow with other Google Cloud services
Master the use of Kubeflow's monitoring and logging capabilities to ensure effective tracking and debugging of machine learning workflows.
Learn best practices for scaling and optimizing machine learning workloads using Kubeflow on Google Cloud
Understand security and governance considerations when working with Kubeflow on Google Cloud

Who is this for?

Cloud Developers interested in using Kubeflow on Google Cloud

What You Need to Know?

Understanding of general Cloud Service Providers (we provide a Google Cloud intro)

Understand general machine learning concepts

Permissions to subscribe to Google Cloud (credit card may be required)

Some Python Programming Experience

More details

Description
Unlock the Power of Machine Learning Workflows on Google Cloud with Kubeflow!
Supercharge your data science skills and revolutionize your machine learning workflows with our comprehensive Udemy course on Kubeflow on Google Cloud. Dive into the world of scalable and portable ML pipelines with this step-by-step guide to harnessing the full potential of Kubeflow.
Master the art of automating end-to-end machine learning workflows using Kubeflow and discover how it seamlessly integrates with the robust infrastructure of Google Cloud. Whether you're a data scientist, ML engineer, or aspiring AI enthusiast, this course equips you with the knowledge and hands-on experience to take your projects to new heights.
What you'll learn:
Unleash the true potential of Kubeflow by understanding its core concepts and its role in building scalable ML workflows.
Deploy and manage Kubeflow pipelines effortlessly to automate and streamline your ML projects on Google Cloud.
Harness the power of Kubeflow's components to optimize hyperparameter tuning and workflow orchestration.
Maximize the potential of Google Cloud's AI Platform for efficient model training and deployment within the Kubeflow ecosystem.
Integrate Kubeflow seamlessly with other Google Cloud services like BigQuery and Cloud Storage for enhanced data processing and storage.
Master the art of monitoring and logging in Kubeflow to ensure the success of your ML projects with real-time insights and debugging capabilities.
Scale and optimize your ML workloads effectively using Kubeflow, leveraging distributed training and resource allocation techniques.
Embrace best practices in security and governance, ensuring compliance and data privacy when working with Kubeflow on Google Cloud.
Don't miss out on this opportunity to become a Kubeflow expert and accelerate your career in the rapidly evolving field of AI and ML. Enroll now and unlock the full potential of Kubeflow on Google Cloud with our comprehensive Udemy course!
Who this course is for:
Cloud Developers interested in using Kubeflow on Google Cloud

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Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings.

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