SkillSoft Explore Course – Lambers, Inc.

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IT Professional Curricula Internet and Network Technologies Solution Area Cloud Computing Google Cloud Platform (GCP) DevOps Automation

In this 18-video course, learners explore features of Google Cloud Platform (GCP), Google Kubernetes Engine (GKE), best practices for operating containers, and how to build cloud-native applications with CloudOps methodology. First, compare Google Cloud Source Repository with GitHub, and see how automated deployment capabilities of Google Cloud Deployment Manager compare with Terraform. Then delve into GCP's machine learning (ML), artificial intelligence, and analytical capabilities and essential design patterns for connecting GCP with other cloud platforms. Discover how to create and configure GKE clusters; deploy applications across multiple Kubernetes clusters; create repositories and manage code with Google Cloud Source Repository; and deploy applications from Cloud Source Repository to App Engine. Next, learn how to automate the configuration of GCP resources and application deployment with Google Cloud Deployment Manager, create virtual machine (VM) instances on GCP; build applications with Terraform; and configure AutoML and BigQuery to manage large volumes of data and build ML models. Finally, learners discover how to set up fully-managed real-time messaging environments, and transfer data between GCP and other Cloud Service providers.

Asset ID	it_cogdoadj_01_enus
Course Type	Video Course
Course Category	Internet and Network Technologies Solution Area

Objectives
GCP DevOps: CloudOps with Google Cloud Platform discover the key concepts covered in this course recognize Google Cloud Platform features and why Google Cloud Platform is a secondary cloud provider describe the key features of Google Kubernetes Engine and how it can be used to set up CloudOps to manage operations that scale and manage workload list best practices for operating containers inspired by the Twelve-Factor App methodology and recognize how to build cloud-native applications using the CloudOps methodology create and configure Google Kubernetes Engine clusters in default VPC and enable alias IP addresses deploy applications across multiple Kubernetes clusters using an Istio multi-cluster service mesh describe the features of Google Cloud Source Repository and compare its capabilities with GitHub create repositories using the Cloud Source Repository and manage code using the checkin/checkout and merge strategies deploy applications from the Cloud Source Repository to App Engine compare the automated deployment capabilities of Google Cloud Deployment Manager and Hashicorp Terraform use the Google Cloud Deployment Manager to automate the configuration of Google Cloud Platform resources to deploy applications on targeted environments use Terraform to create virtual machine instances on the Google Cloud Platform and build applications on the instances recognize the machine learning, artificial intelligence, and analytical capabilities of Google Cloud Platform, with focus on the toolsets that enable edge feature implementations like artificial intelligence, IoT, and business intelligence configure AutoML and BigQuery to manage large scale of data and provide high quality machine learning models following the CloudOps paradigm configure Cloud Pub/Sub to set up fully-managed real-time messaging environments to send and receive messages between independent applications recognize the concept of multi-cloud design along with the essential design patterns for connecting Google Cloud Platform with other cloud platforms establish connectivity and transfer data between Google Cloud Platform and other cloud service providers using external IP addresses summarize the key concepts covered in this course

Objectives

GCP DevOps: CloudOps with Google Cloud Platform

discover the key concepts covered in this course
recognize Google Cloud Platform features and why Google Cloud Platform is a secondary cloud provider
describe the key features of Google Kubernetes Engine and how it can be used to set up CloudOps to manage operations that scale and manage workload
list best practices for operating containers inspired by the Twelve-Factor App methodology and recognize how to build cloud-native applications using the CloudOps methodology
create and configure Google Kubernetes Engine clusters in default VPC and enable alias IP addresses
deploy applications across multiple Kubernetes clusters using an Istio multi-cluster service mesh
describe the features of Google Cloud Source Repository and compare its capabilities with GitHub
create repositories using the Cloud Source Repository and manage code using the checkin/checkout and merge strategies
deploy applications from the Cloud Source Repository to App Engine
compare the automated deployment capabilities of Google Cloud Deployment Manager and Hashicorp Terraform
use the Google Cloud Deployment Manager to automate the configuration of Google Cloud Platform resources to deploy applications on targeted environments
use Terraform to create virtual machine instances on the Google Cloud Platform and build applications on the instances
recognize the machine learning, artificial intelligence, and analytical capabilities of Google Cloud Platform, with focus on the toolsets that enable edge feature implementations like artificial intelligence, IoT, and business intelligence
configure AutoML and BigQuery to manage large scale of data and provide high quality machine learning models following the CloudOps paradigm
configure Cloud Pub/Sub to set up fully-managed real-time messaging environments to send and receive messages between independent applications
recognize the concept of multi-cloud design along with the essential design patterns for connecting Google Cloud Platform with other cloud platforms
establish connectivity and transfer data between Google Cloud Platform and other cloud service providers using external IP addresses
summarize the key concepts covered in this course