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KubeFlow Bootcamp

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KubeFlow Bootcamp, available at $79.99, has an average rating of 4.15, with 56 lectures, based on 210 reviews, and has 1953 subscribers.

Rating4.2
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You will learn about 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 This course is ideal for individuals who are Cloud Developers interested in using Kubeflow on Google Cloud It is particularly useful for Cloud Developers interested in using Kubeflow on Google Cloud.

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Summary

Title: KubeFlow Bootcamp

Price: $79.99

Average Rating: 4.15

Number of Lectures: 56

Number of Published Lectures: 55

Number of Curriculum Items: 56

Number of Published Curriculum Objects: 55

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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 Should Attend

  • Cloud Developers interested in using Kubeflow on Google Cloud

Target Audiences

  • Cloud Developers interested in using Kubeflow on Google Cloud

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:

  1. Unleash the true potential of Kubeflow by understanding its core concepts and its role in building scalable ML workflows.

  2. Deploy and manage Kubeflow pipelines effortlessly to automate and streamline your ML projects on Google Cloud.

  3. Harness the power of Kubeflow’s components to optimize hyperparameter tuning and workflow orchestration.

  4. Maximize the potential of Google Cloud’s AI Platform for efficient model training and deployment within the Kubeflow ecosystem.

  5. Integrate Kubeflow seamlessly with other Google Cloud services like BigQuery and Cloud Storage for enhanced data processing and storage.

  6. Master the art of monitoring and logging in Kubeflow to ensure the success of your ML projects with real-time insights and debugging capabilities.

  7. Scale and optimize your ML workloads effectively using Kubeflow, leveraging distributed training and resource allocation techniques.

  8. 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!

Course Curriculum

Chapter 1: Course Introduction

Lecture 1: FAQ AND COURSE DOWNLOADS

Lecture 2: Course Overview

Chapter 2: Google Cloud Overview and Setup

Lecture 1: Overview of Google Cloud Section

Lecture 2: What is Cloud? A Cloud Computing Overview

Lecture 3: GCP Network Infrastructure

Lecture 4: GCP Network Connections

Lecture 5: Why Choose GCP?

Lecture 6: Google Cloud Account Set-Up

Lecture 7: Billing and Budgets

Lecture 8: Billing Tour: DEMO

Lecture 9: Setting a Budget Alert: DEMO

Lecture 10: Google Cloud Storage Options

Lecture 11: Cloud Storage Overview

Lecture 12: Cloud Storage DEMO

Chapter 3: Kubernetes Overview

Lecture 1: Introduction to Kubernetes Section

Lecture 2: Kubernetes Basics Overview

Lecture 3: Understanding Containers

Lecture 4: Understanding Nodes and Control Plane

Lecture 5: Understanding Kubernetes API

Lecture 6: Container Images

Lecture 7: Cloud Build DEMO

Lecture 8: Google Kubernetes Engine – GKE

Lecture 9: Kubernetes Architecture

Lecture 10: GKE via GUI DEMO

Lecture 11: Kubectl

Lecture 12: GKE via Command Line – DEMO

Lecture 13: Kubernetes Deployments

Lecture 14: Kubernetes Deployment Updates

Lecture 15: Kubernetes Deployment Strategies

Lecture 16: Kubernetes Deployment – DEMO

Lecture 17: Kubernetes Pod Networking

Lecture 18: Kubernetes Storage with Volumes

Lecture 19: Volume Storage – DEMO

Chapter 4: Understanding Machine Learning Fundamentals

Lecture 1: Introduction to Machine Learning Fundamentals

Lecture 2: Machine Learning Pathway

Lecture 3: Why Machine Learning?

Lecture 4: Types of Machine Learning Algorithms

Lecture 5: Supervised Learning Process

Lecture 6: AI vs ML

Lecture 7: AI and ML on Google Cloud

Lecture 8: Vertex AI Overview

Lecture 9: Vertex AI Workbench: Notebooks in the Cloud

Chapter 5: Kubeflow Pipelines with Google Cloud

Lecture 1: Kubeflow Section Overview

Lecture 2: Vertex AI Pipelines

Lecture 3: Vertex AI Pipelines and SDKs

Lecture 4: Vertex AI Pipeline DEMO

Lecture 5: AI Pipeline via Notebook DEMO

Lecture 6: Kubeflow Overview

Lecture 7: Kubeflow DSL

Lecture 8: Kubeflow Pre-Built Components

Lecture 9: Kubeflow Python Components

Lecture 10: Kubeflow Custom Components

Lecture 11: Kubeflow Compilation

Lecture 12: Machine Learning Kubeflow Pipeline DEMO

Lecture 13: Continuous Training with Kubeflow

Instructors

Rating Distribution

  • 1 stars: 15 votes
  • 2 stars: 7 votes
  • 3 stars: 23 votes
  • 4 stars: 66 votes
  • 5 stars: 101 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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