KubeFlow Bootcamp
KubeFlow Bootcamp, available at $79.99, has an average rating of 4.15, with 56 lectures, based on 210 reviews, and has 1953 subscribers.
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.
Enroll now: KubeFlow Bootcamp
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:
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Unleash the true potential of Kubeflow by understanding its core concepts and its role in building scalable ML workflows.
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Deploy and manage Kubeflow pipelines effortlessly to automate and streamline your ML projects on Google Cloud.
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Harness the power of Kubeflow’s components to optimize hyperparameter tuning and workflow orchestration.
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Maximize the potential of Google Cloud’s AI Platform for efficient model training and deployment within the Kubeflow ecosystem.
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Integrate Kubeflow seamlessly with other Google Cloud services like BigQuery and Cloud Storage for enhanced data processing and storage.
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Master the art of monitoring and logging in Kubeflow to ensure the success of your ML projects with real-time insights and debugging capabilities.
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Scale and optimize your ML workloads effectively using Kubeflow, leveraging distributed training and resource allocation techniques.
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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
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Jose Portilla
Head of Data Science at Pierian Training -
Pierian Training
Data Science and Machine Learning Training
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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