GCP DevOps: CloudOps with Google Cloud Platform
Google Cloud 2020
| Intermediate
- 18 Videos | 1h 51m 22s
- Includes Assessment
- Earns a Badge
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.
WHAT YOU WILL LEARN
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discover the key concepts covered in this courserecognize Google Cloud Platform features and why Google Cloud Platform is a secondary cloud providerdescribe the key features of Google Kubernetes Engine and how it can be used to set up CloudOps to manage operations that scale and manage workloadlist best practices for operating containers inspired by the Twelve-Factor App methodology and recognize how to build cloud-native applications using the CloudOps methodologycreate and configure Google Kubernetes Engine clusters in default VPC and enable alias IP addressesdeploy applications across multiple Kubernetes clusters using an Istio multi-cluster service meshdescribe the features of Google Cloud Source Repository and compare its capabilities with GitHubcreate repositories using the Cloud Source Repository and manage code using the checkin/checkout and merge strategiesdeploy applications from the Cloud Source Repository to App Engine
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compare the automated deployment capabilities of Google Cloud Deployment Manager and Hashicorp Terraformuse the Google Cloud Deployment Manager to automate the configuration of Google Cloud Platform resources to deploy applications on targeted environmentsuse Terraform to create virtual machine instances on the Google Cloud Platform and build applications on the instancesrecognize 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 intelligenceconfigure AutoML and BigQuery to manage large scale of data and provide high quality machine learning models following the CloudOps paradigmconfigure Cloud Pub/Sub to set up fully-managed real-time messaging environments to send and receive messages between independent applicationsrecognize the concept of multi-cloud design along with the essential design patterns for connecting Google Cloud Platform with other cloud platformsestablish connectivity and transfer data between Google Cloud Platform and other cloud service providers using external IP addressessummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview1m 38sUP NEXT
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2.GCP Feature Comparison4m 53s
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3.Features of GKE for Operations5m 59s
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4.Cloud Native with CloudOps8m 50s
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5.Creating and Configuring GKE5m 42s
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6.Deploying Apps across Multiple GKE Clusters8m 20s
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7.Google Cloud Source Repository5m 36s
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8.Creating the Repository and Managing Code8m 14s
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9.Deploying Apps from Cloud Source Repository4m 19s
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10.Cloud Deployment Manager vs. Terraform6m 34s
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11.Using Deployment Manager for Automation5m 14s
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12.Using Terraform for Automation8m 41s
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13.ML and AI with Google Cloud Platform6m 43s
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14.Using AutoML and BigQuery for ML Ops6m 51s
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15.Messaging in GCP5m 31s
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16.Multi-Cloud Design Pattern Powered by GCP5m 13s
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17.Transferring Data from GCP to Other Cloud Providers11m 23s
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18.Course Summary1m 41s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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