Getting started with Google Kubernetes Engine Training

NOTE: You'll learn to containerize workloads in Docker containers, deploy them to Kubernetes clusters provided by Google Kubernetes Engine, and scale those workloads to handle increased traffic.

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Course Description

Learn how to create and deploy containerized applications on Google Kubernetes Engine (GKE). This course features a combination of lectures, demos, and hands-on labs to help you explore and deploy solution elements —including infrastructure components like pods, and containers.

Learning Objectives

This course teaches participants the following skills:

Understand how software containers work.
Understand the architecture of Kubernetes.
Understand the architecture of Google Cloud.

Understand how pod networking works in Kubernetes Engine.
Create Kubernetes Engine clusters using the Google Cloud Console and gcloud/ kubectl commands.

Certification Curriculum

The course includes presentations, demonstrations, and hands-on labs.

Module 1

Introduction to Google Cloud

Use the Google Cloud Console.
Use Cloud Shell.
Define Cloud Computing.
Identify Google Cloud compute services.
Understand regions and zones.
Understand the Cloud resource hierarchy.
Administer your Google Cloud resources.

Module 2

Containers and Kubernetes in Google Cloud

Create a container using Cloud Build.
Store a container in the Container Registry.
Understand the relationship between Kubernetes and Google Kubernetes Engine (GKE).
Understand how to choose among Google Cloud Compute platforms.

Module 3

Kubernetes Architecture

Understand the architecture of Kubernetes: pods, namespaces.
Understand the control-plane components of Kubernetes.
Create container images using Cloud Build.
Store container images in Container Registry.
Create a Kubernetes engine cluster.

Module 4

Introduction to Kubernetes Workloads

The kubectl command.
Introduction to deployments.
Pod networking.
Volume overview.

Prerequisites

To get the most out of this course, participants should have:

Completed Google Cloud Fundamentals: Core Infrastructure or have equivalent experience.
Basic proficiency with command-line tools and Linux operating system environments

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Enrol in the Getting Started with Google Kubernetes Engine Training and gain the knowledge and skills needed to advance your career. A Path to In-Demand Jobs.

Certification Assessment

When you complete this program, you’ll earn a Certificate to share with your professional network as well as unlock access to career support resources to help you kickstart your new career. Many Professional Certificates have hiring partners that recognize the Professional Certificate credential and others can help prepare you for a certification exam. You can find more information on individual Professional Certificate pages where it applies.

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Testimonials

Our clients praise us for our great results, personable service, expert knowledge, and on-time delivery. Here are what just a few of them had to say:

Sarbojit Bose

The course is one of the two in the track of Agile Professional Coach. It is designed to provide both wide and deep knowledge to become a competent Coach with the additional skills of a Trainer and a Mentor. The two trainers, Preeth Panday and Naveen K Singh, are excellent Facilitators and Coaches with patience and promptness.

Tiong Yeow Tan

It was a good introductory course to agile coaching, providing a good overview and potential deep dive into other courses if one wishes to learn more about a particular topic. Trainer was able to address most of the questions from the participants and demonstrate coaching in his interaction with us.

Chun Ngee Boo Alson

The course is well structure. Timing is also right. The trainer Mr Raj is professional. And he asnwer all my question and doubts.

Puli

One of best I have encountered in my life. Freedom to interact and respond candidly and with courage for every question is not an easy task for Trainers which they did it exceptionally well.

Training FAQ's

What is Machine Learning Training all about?

In the Machine Learning Training, you will learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models and offer high-performance predictions

What is the duration of Machine Learning Training?

The duration of Machine Learning Training is two-days.

Who can take up the Machine Learning Training?

Data Engineers and programmers interested in learning how to apply machine learning in practice or anyone interested in learning how to build and operationalize TensorFlow models can apply for Machine Learning Training.

What is the benefit of taking up the Machine LearningTraining?

With Machine Learning Training with TensorFlow Certification, you can strengthen your cloud knowledge, earn a digital certificate, and start preparing for an industry-recognized Google Cloud certification.

What are the prerequisites of Machine LearningTraining?

Participants should have experience coding in Python, knowledge of basic statistics, and knowledge of SQL and cloud computing.

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Getting Started with Google Kubernetes Engine Training