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Google Cloud Platform Essential Training (2019)

via LinkedIn Learning

Overview

Explore the Google cloud. Learn how to leverage Google Cloud Platform services—including serverless computing, data storage, and machine learning—for your organization.

Is Google Cloud Platform (GCP) right for your business? Explore the offerings of this leading cloud services provider. This course is designed for IT professionals—architects, network admins, and technology stakeholders—evaluating GCP for their organizations as well as those tasked with getting apps up and running on the Google cloud. Learn how to set up an account and use the console, APIs, Cloud Shell, and other GCP tools. Then explore the core services—serverless computing, data storage, and machine learning—and the developer tools that Google provides for building your own custom solutions. Along the way, instructor Lynn Langit shows how to use GCP to manage virtual machines, Docker containers, Kubernetes clusters, functions, relational data stores, NoSQL data, and more. She also shares practical tips for saving money and planning deployments, and reviews examples of common architectural patterns.

Syllabus

Introduction
  • Hosting your application on Google Cloud Platform
  • What you should know
  • A note about working with cloud services
1. Why Use Google Cloud?
  • Understanding Google Cloud Platform
  • Save time and money with Google Cloud Platform
  • Auto-scale on Google Cloud Platform
  • Compliance
  • Service categories: Compute, data, and more
  • Tools and libraries
2. Getting Started
  • Set up Google Cloud Platform account
  • Navigate the Google Cloud Platform console
  • Work with projects
  • Use APIs
  • Use regions and zones
  • Understand Google Cloud billing
  • Use Google Cloud Security, IAM
  • Use the integrated Google Cloud console
  • Use local Google Cloud
  • Use walkthroughs and tutorials
3. Compute
  • Google Compute Engine
  • Google Cloud Platform Marketplace to set up Redis
  • GCE Resources
  • Kubernetes Engine and GKE containers
  • Kubernetes Engine and GKE clusters
  • Google Cloud Run
  • Google Cloud Run on GKE
  • Google Cloud Functions
4. Storage and Database
  • Google Cloud storage
  • Google Cloud SQL
  • Google BigQuery
  • Google Marketplace Datasets
  • Google BigTable
  • Google MemoryStore
  • Google Firestore (formerly Datastore)
  • Google Pub/Sub
5. Machine Learning and Data Movement
  • Google Dataprep
  • Google Data Fusion
  • Google Data Studio
  • AI Platform and Hub
  • Google Jupyter Notebooks
  • TensorFlow 2.0 in Colab notebooks
  • Cloud Vision API
  • Cloud AutoML Tables
  • Data Science VM
6. Developer Tools
  • Deployment Manager and Marketplace
  • Cloud Scheduler and Tasks
  • Cloud Code
  • Stackdriver logs
  • Stackdriver Monitoring
7. Key Architectures
  • Google Cloud Platform architecture concepts
  • Web single-page and API-based architectures
  • Web blog and managed databases
  • Web application on GAE
  • Mobile development with AI
  • Big Data log processing
Conclusion
  • Next steps

Taught by

Lynn Langit

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