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Google Cloud Training
This is a self-paced lab that takes place in the Google Cloud console.
In this hands-on lab you will explore using Google Cloud Kubernetes Engine and Kubeflow TFJob to scale out TensorFlow distributed training.
Register for this course and see more details by visiting:
OpenCourser.com/course/za9yvb/distributed
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Students develop proficiency in training machine learning models with Google Cloud Kubernetes Engine and Kubeflow
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Save Distributed Multi-worker TensorFlow Training on Kubernetes to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Distributed Multi-worker TensorFlow Training on Kubernetes with these
activities:
Review Kubernetes fundamentals before starting the course
Show steps
Ensures that students have a solid understanding of Kubernetes concepts before embarking on the course.
Show steps
-
Read introductory articles or tutorials on Kubernetes
-
Review key concepts such as pods, containers, and services
-
Try out basic Kubernetes commands on a local cluster or playground
Review Google Cloud console
Show steps
Familiarize yourself with the Google Cloud console to enhance your understanding of the lab environment.
Browse courses on
Google Cloud Console
Show steps
-
Log in to Google Cloud console
-
Explore different services and resources
-
Create a new project
Follow official Google Cloud tutorials
Show steps
Supplement your learning by following official Google Cloud tutorials relevant to the course.
Browse courses on
Kubernetes Engine
Show steps
-
Search for tutorials on Kubeflow TFJob
-
Complete the "Create a TFJob" tutorial
-
Explore other related tutorials
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Practice using Kubernetes pods and services
Show steps
Review the basics of Kubernetes pods and services by testing their ability to spin up a Kubernetes pod with a simple deployment and service.
Show steps
-
Create a Kubernetes pod using the provided YAML file
-
Expose the pod using a Kubernetes service
-
Test the pod and service by making HTTP requests
Participate in a Kubernetes study group
Show steps
Engage with peers, share knowledge, and collaborate on Kubernetes-related projects.
Show steps
-
Find a study group or create one with fellow students
-
Meet regularly to discuss Kubernetes concepts and work on projects together
-
Share resources, ask questions, and provide feedback to each other
Follow the Google Cloud Kubernetes Engine tutorial
Show steps
Reinforce their understanding of Kubernetes by following the structured tutorial provided by Google Cloud.
Show steps
-
Read through the tutorial and set up the prerequisites
-
Follow the step-by-step instructions to create a Kubernetes cluster
-
Deploy a sample application to the cluster
Deploy multiple TFJobs
Show steps
Gain practical experience by deploying multiple TFJobs to simulate real-world scenarios.
Browse courses on
Kubernetes
Show steps
-
Create a configuration file for the TFJobs
-
Submit multiple TFJobs using the configuration file
-
Monitor the progress and results of the TFJobs
Build a simple Flask application using Kubernetes
Show steps
Apply their knowledge of Kubernetes by creating a real-world application that leverages the platform's capabilities.
Show steps
-
Create a simple Flask application with basic functionality
-
Dockerize the Flask application to make it portable
-
Create a Kubernetes deployment and service for the application
-
Deploy the application to a Kubernetes cluster
-
Validate the application's functionality and familiarize yourself with Kubernetes logs
Solve Kubernetes hands-on labs
Show steps
Enhance their practical skills by completing hands-on labs that test their ability to manage and troubleshoot Kubernetes clusters.
Show steps
-
Access the Kubernetes hands-on labs provided by Google Cloud
-
Work through the labs to solve challenges related to Kubernetes management and troubleshooting
-
Review the solutions and identify areas for improvement
Mentor junior developers in Kubernetes
Show steps
Sharpen their Kubernetes skills by guiding and assisting others who are new to the platform.
Show steps
-
Identify junior developers who could benefit from mentorship
-
Offer guidance, answer questions, and provide support on Kubernetes-related topics
-
Review their code, provide feedback, and suggest improvements
Contribute to open-source Kubernetes projects
Show steps
Make meaningful contributions to the Kubernetes community by participating in open-source projects.
Show steps
-
Explore open-source Kubernetes projects and identify areas where they can contribute
-
Join the project's community and engage in discussions
-
Submit bug reports, feature requests, or code contributions
Build a machine learning model with Kubernetes Engine and TensorFlow
Show steps
Apply your knowledge by building a complete machine learning solution using Kubernetes Engine and TensorFlow.
Browse courses on
Machine Learning
Show steps
-
Define the problem statement and gather data
-
Choose and train a machine learning model
-
Deploy the model on Kubernetes Engine
-
Evaluate the performance of the model
Review Kubernetes fundamentals before starting the course
Show steps
Ensures that students have a solid understanding of Kubernetes concepts before embarking on the course.
Show steps
Show steps
Show steps
- Read introductory articles or tutorials on Kubernetes
- Review key concepts such as pods, containers, and services
- Try out basic Kubernetes commands on a local cluster or playground
Review Google Cloud console
Show steps
Familiarize yourself with the Google Cloud console to enhance your understanding of the lab environment.
Browse courses on
Google Cloud Console
Show steps
Show steps
Show steps
- Log in to Google Cloud console
- Explore different services and resources
- Create a new project
Follow official Google Cloud tutorials
Show steps
Supplement your learning by following official Google Cloud tutorials relevant to the course.
Browse courses on
Kubernetes Engine
Show steps
Show steps
Show steps
- Search for tutorials on Kubeflow TFJob
- Complete the "Create a TFJob" tutorial
- Explore other related tutorials
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Practice using Kubernetes pods and services
Show steps
Review the basics of Kubernetes pods and services by testing their ability to spin up a Kubernetes pod with a simple deployment and service.
Show steps
Show steps
Show steps
- Create a Kubernetes pod using the provided YAML file
- Expose the pod using a Kubernetes service
- Test the pod and service by making HTTP requests
Participate in a Kubernetes study group
Show steps
Engage with peers, share knowledge, and collaborate on Kubernetes-related projects.
Show steps
Show steps
Show steps
- Find a study group or create one with fellow students
- Meet regularly to discuss Kubernetes concepts and work on projects together
- Share resources, ask questions, and provide feedback to each other
Follow the Google Cloud Kubernetes Engine tutorial
Show steps
Reinforce their understanding of Kubernetes by following the structured tutorial provided by Google Cloud.
Show steps
Show steps
Show steps
- Read through the tutorial and set up the prerequisites
- Follow the step-by-step instructions to create a Kubernetes cluster
- Deploy a sample application to the cluster
Deploy multiple TFJobs
Show steps
Gain practical experience by deploying multiple TFJobs to simulate real-world scenarios.
Browse courses on
Kubernetes
Show steps
Show steps
Show steps
- Create a configuration file for the TFJobs
- Submit multiple TFJobs using the configuration file
- Monitor the progress and results of the TFJobs
Build a simple Flask application using Kubernetes
Show steps
Apply their knowledge of Kubernetes by creating a real-world application that leverages the platform's capabilities.
Show steps
Show steps
Show steps
- Create a simple Flask application with basic functionality
- Dockerize the Flask application to make it portable
- Create a Kubernetes deployment and service for the application
- Deploy the application to a Kubernetes cluster
- Validate the application's functionality and familiarize yourself with Kubernetes logs
Solve Kubernetes hands-on labs
Show steps
Enhance their practical skills by completing hands-on labs that test their ability to manage and troubleshoot Kubernetes clusters.
Show steps
Show steps
Show steps
- Access the Kubernetes hands-on labs provided by Google Cloud
- Work through the labs to solve challenges related to Kubernetes management and troubleshooting
- Review the solutions and identify areas for improvement
Mentor junior developers in Kubernetes
Show steps
Sharpen their Kubernetes skills by guiding and assisting others who are new to the platform.
Show steps
Show steps
Show steps
- Identify junior developers who could benefit from mentorship
- Offer guidance, answer questions, and provide support on Kubernetes-related topics
- Review their code, provide feedback, and suggest improvements
Contribute to open-source Kubernetes projects
Show steps
Make meaningful contributions to the Kubernetes community by participating in open-source projects.
Show steps
Show steps
Show steps
- Explore open-source Kubernetes projects and identify areas where they can contribute
- Join the project's community and engage in discussions
- Submit bug reports, feature requests, or code contributions
Build a machine learning model with Kubernetes Engine and TensorFlow
Show steps
Apply your knowledge by building a complete machine learning solution using Kubernetes Engine and TensorFlow.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Define the problem statement and gather data
- Choose and train a machine learning model
- Deploy the model on Kubernetes Engine
- Evaluate the performance of the model
Career center
Learners who complete Distributed Multi-worker TensorFlow Training on Kubernetes will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their expertise in mathematics, computer science, and statistics to extract insights from data that can help businesses make better decisions. This course can help you develop the skills you need to become a successful Data Scientist by teaching you how to use Google Cloud Kubernetes Engine and Kubeflow TFJob to scale out TensorFlow distributed training.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models that can be used to solve a variety of business problems. This course can help you develop the skills you need to become a successful Machine Learning Engineer by teaching you how to use Google Cloud Kubernetes Engine and Kubeflow TFJob to scale out TensorFlow distributed training.
Cloud Engineer
Cloud Engineer
Cloud Engineers design, build, and maintain cloud-based applications and infrastructure. This course can help you develop the skills you need to become a successful Cloud Engineer by teaching you how to use Google Cloud Kubernetes Engine and Kubeflow TFJob to scale out TensorFlow distributed training.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software applications. This course can help you develop the skills you need to become a successful Software Engineer by teaching you how to use Google Cloud Kubernetes Engine and Kubeflow TFJob to scale out TensorFlow distributed training.
Business Analyst
Business Analyst
Business Analysts help businesses understand their customers and make better decisions. This course can help you develop the skills you need to become a successful Business Analyst by teaching you how to use Google Cloud Kubernetes Engine and Kubeflow TFJob to scale out TensorFlow distributed training.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses understand their customers and make better decisions. This course can help you develop the skills you need to become a successful Data Analyst by teaching you how to use Google Cloud Kubernetes Engine and Kubeflow TFJob to scale out TensorFlow distributed training.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. This course can help you develop the skills you need to become a successful Project Manager by teaching you how to use Google Cloud Kubernetes Engine and Kubeflow TFJob to scale out TensorFlow distributed training.
Product Manager
Product Manager
Product Managers plan, develop, and launch products. This course can help you develop the skills you need to become a successful Product Manager by teaching you how to use Google Cloud Kubernetes Engine and Kubeflow TFJob to scale out TensorFlow distributed training.
Systems Engineer
Systems Engineer
Systems Engineers design, build, and maintain complex systems. This course can help you develop the skills you need to become a successful Systems Engineer by teaching you how to use Google Cloud Kubernetes Engine and Kubeflow TFJob to scale out TensorFlow distributed training.
Sales Engineer
Sales Engineer
Sales Engineers help customers understand and buy technical products. This course can help you develop the skills you need to become a successful Sales Engineer by teaching you how to use Google Cloud Kubernetes Engine and Kubeflow TFJob to scale out TensorFlow distributed training.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products. This course can help you develop the skills you need to become a successful Technical Writer by teaching you how to use Google Cloud Kubernetes Engine and Kubeflow TFJob to scale out TensorFlow distributed training.
Security Engineer
Security Engineer
Security Engineers design, build, and maintain security systems. This course can help you develop the skills you need to become a successful Security Engineer by teaching you how to use Google Cloud Kubernetes Engine and Kubeflow TFJob to scale out TensorFlow distributed training.
Network Engineer
Network Engineer
Network Engineers design, build, and maintain computer networks. This course can help you develop the skills you need to become a successful Network Engineer by teaching you how to use Google Cloud Kubernetes Engine and Kubeflow TFJob to scale out TensorFlow distributed training.
Database Administrator
Database Administrator
Database Administrators design, build, and maintain databases. This course can help you develop the skills you need to become a successful Database Administrator by teaching you how to use Google Cloud Kubernetes Engine and Kubeflow TFJob to scale out TensorFlow distributed training.
IT Manager
IT Manager
IT Managers plan, organize, and direct the activities of an IT department. This course may be useful to you if you are interested in becoming an IT Manager, as it can help you develop the skills you need to manage large-scale IT projects.
For more career information including salaries, visit:
OpenCourser.com/course/za9yvb/distributed
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Distributed Multi-worker TensorFlow Training on Kubernetes.
Save
A comprehensive guide to Kubernetes, covering architecture, components, and best practices. Provides foundational knowledge for leveraging Kubernetes in distributed training environments.
A comprehensive guide to Kubernetes, covering architecture, deployment, and management. Provides a solid foundation for understanding the infrastructure used for distributed training.
A comprehensive guide to TensorFlow 2.0, covering fundamental concepts, model building, and deployment. Provides a solid foundation for distributed training.
Save
Covers the principles and techniques for designing and building data-intensive applications. It provides insights into data modeling, storage systems, and distributed computing, which are relevant to the course content on TensorFlow training at scale.
Provides detailed recipes covering a variety of TensorFlow 2.0 tasks, including data preprocessing, model building, training, and deployment.
Save
Provides best practices for designing, implementing, and operating Kubernetes clusters in production environments. It covers topics such as cluster architecture, security, monitoring, and troubleshooting, which are relevant to the course content on training TensorFlow on Kubernetes.
Focuses on microservices architecture and design patterns. It provides insights into building and managing microservices-based applications, which is relevant to the course content on TensorFlow training in a distributed environment.
A collection of practical projects and tutorials to apply TensorFlow in real-world scenarios. Provides hands-on experience with distributed training and scalable solutions.
A guide to deploying and managing machine learning models in production. Provides insights into scaling, monitoring, and continuous delivery, which are essential for distributed training.
A gentle introduction to machine learning concepts and algorithms. Provides a foundation for understanding the underlying principles used in distributed training.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/za9yvb/distributed
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