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Real-time code analysis and feedback
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.
The course includes presentations, demonstrations, and hands-on labs.
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.
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.
- 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.
Introduction to Kubernetes Workloads
- The kubectl command.
- Introduction to deployments.
- Pod networking.
- Volume overview.
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
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.
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.
- High Success rate
- Join Our Dynamic Community
- Training from Recognized Trainer
- Post-workshop support by the Coaches
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:
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
The duration of Machine Learning Training is two-days.
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.
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.
Participants should have experience coding in Python, knowledge of basic statistics, and knowledge of SQL and cloud computing.