Introduction to Kubeflow: Fundamentals
Kubeflow Training and Certification is available free of charge, providing an introduction to the fundamentals of the Kubeflow platform. Participants will gain an understanding of the core concepts and components of Kubeflow. ▼
ADVERTISEMENT
Course Feature
Cost:
Free
Provider:
Udemy
Certificate:
No Information
Language:
English
Course Overview
❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [March 06th, 2023]
Learners can learn about the Kubeflow architecture and its components, such as the Kubeflow platform, tools, and add-ons. They can also learn how to install Kubeflow on AWS, Google Cloud Platform, and locally. Additionally, learners can gain an understanding of how the components of Kubeflow work together to create a powerful platform for machine learning and data science. They can also learn how to use the tools and add-ons to enhance the Kubeflow platform and make it more efficient. Finally, learners can gain an understanding of the best practices for deploying and managing Kubeflow in production.
[Applications]
After completing this course, students should be able to apply the knowledge they have gained to develop and deploy machine learning models using Kubeflow. They should be able to use the Kubeflow platform to create and manage machine learning pipelines, deploy models to production, and monitor their performance. Additionally, they should be able to use the Kubeflow add-ons to enhance their machine learning pipelines and improve their model performance. Finally, they should be able to install Kubeflow on AWS, Google Cloud Platform, and locally.
[Career Paths]
1. Kubeflow Developer: Kubeflow developers are responsible for designing, developing, and deploying Kubeflow applications. They must have a strong understanding of the Kubeflow architecture and be able to use the tools and add-ons to enhance the platform. This job is in high demand as more companies are looking to leverage the power of Kubeflow for their machine learning and AI projects.
2. Kubeflow Administrator: Kubeflow administrators are responsible for managing and maintaining the Kubeflow platform. They must have a strong understanding of the Kubeflow architecture and be able to install and configure the platform on various cloud platforms. This job is in high demand as more companies are looking to leverage the power of Kubeflow for their machine learning and AI projects.
3. Kubeflow Data Scientist: Kubeflow data scientists are responsible for designing and developing machine learning and AI models using the Kubeflow platform. They must have a strong understanding of the Kubeflow architecture and be able to use the tools and add-ons to enhance the platform. This job is in high demand as more companies are looking to leverage the power of Kubeflow for their machine learning and AI projects.
4. Kubeflow Consultant: Kubeflow consultants are responsible for providing advice and guidance to companies looking to leverage the power of Kubeflow for their machine learning and AI projects. They must have a strong understanding of the Kubeflow architecture and be able to use the tools and add-ons to enhance the platform. This job is in high demand as more companies are looking to leverage the power of Kubeflow for their machine learning and AI projects.
[Education Paths]
1. Bachelor of Science in Computer Science: This degree path provides students with a comprehensive understanding of computer science fundamentals, including programming, software engineering, and data structures. Students will also learn about the latest trends in artificial intelligence, machine learning, and cloud computing.
2. Master of Science in Artificial Intelligence: This degree path focuses on the development of artificial intelligence systems and their applications. Students will learn about the latest AI technologies, such as deep learning, natural language processing, and computer vision. They will also gain experience in developing AI-based applications and systems.
3. Master of Science in Cloud Computing: This degree path focuses on the development of cloud-based applications and systems. Students will learn about the latest cloud technologies, such as containerization, serverless computing, and distributed computing. They will also gain experience in developing cloud-based applications and systems.
4. Master of Science in Data Science: This degree path focuses on the development of data-driven applications and systems. Students will learn about the latest data science technologies, such as data mining, machine learning, and predictive analytics. They will also gain experience in developing data-driven applications and systems.
Course Syllabus
Machine Learning Workflows
Kubeflow Components
Kubeflow Tools and Add-ons
Kubeflow Distributions
Kubeflow Community
Pros & Cons
Thorough and concise instructor.
Real world examples of getting ML model to production.
Good quick summary.
Nice first overview.
Poorly edited video.
Lecturer talks too quickly.
Video/sound sync issue.
Couldn't manage to merge videos.
Course Provider
Provider Udemy's Stats at AZClass
Discussion and Reviews
0.0 (Based on 0 reviews)
Explore Similar Online Courses
How to Convert Excel to a Custom Web Application with Caspio
Using Effcient Sorting Algorithms in Java to Arrange Tax Data
Python for Informatics: Exploring Information
Social Network Analysis
Introduction to Systematic Review and Meta-Analysis
The Analytics Edge
DCO042 - Python For Informatics
Causal Diagrams: Draw Your Assumptions Before Your Conclusions
Whole genome sequencing of bacterial genomes - tools and applications
How I would learn Machine Learning (if I could start over)
Learn Data Science and Machine Learning on Microsoft Azure
Machine Learning for Everyone
Related Categories
Popular Providers
Quiz
Submitted Sucessfully
1. What is Kubeflow?
2. What is the purpose of Kubeflow?
3. Which of the following is a component of Kubeflow?
Start your review of Introduction to Kubeflow: Fundamentals