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Google Cloud

Container Workload Management and Production Operations

Google Cloud via Coursera Specialization

Overview

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Running containerized applications in production requires more than deploying a Kubernetes cluster. In Container Workload Management and Production Operations, you'll build hands-on Google Kubernetes Engine (GKE) skills for deploying, scaling, securing, monitoring, and managing containerized workloads. You'll progress from Kubernetes and GKE fundamentals to production operations such as deployment management, workload optimization, autoscaling, monitoring, backup and restore, security hardening, and multi-tenant cluster management. You'll also explore how GKE Fleets and managed service mesh capabilities support applications distributed across multiple clusters. By the end of this specialization, you'll be able to: Create and manage GKE clusters and deploy containerized applications Configure and scale Kubernetes deployments using kubectl and YAML Optimize workloads for availability, performance, resource use, and cost Apply autoscaling, monitoring, backup, and recovery strategies to GKE workloads Harden GKE clusters and manage access in multi-tenant environments Manage workloads and distributed services across multiple GKE clusters

Syllabus

  • Course 1: Getting Started with Google Kubernetes Engine
  • Course 2: Kubernetes Engine: Qwik Start
  • Course 3: GKE Autopilot: Qwik Start
  • Course 4: Managing Deployments Using Kubernetes Engine
  • Course 5: GKE Workload Optimization
  • Course 6: Understanding and Combining GKE Autoscaling Strategies
  • Course 7: Manage Scalable Workloads in GKE
  • Course 8: Hardening Default GKE Cluster Configurations
  • Course 9: Cloud Operations for GKE
  • Course 10: Managing a GKE Multi-tenant Cluster with Namespaces
  • Course 11: GKE Backup and Restore
  • Course 12: Manage Multi-cluster Workloads at Scale with GKE Fleets and Teams
  • Course 13: Manage and Secure Distributed Services with GKE Managed Service Mesh

Courses

Taught by

Google Cloud Training

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