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Learn Google Cloud Compute, earn certificates with free online courses from CodeSignal, Coursera, Udemy and other top learning platforms around the world. Read reviews to decide if a class is right for you.
Deploy and manage GCP Compute Engine instances and Cloud Storage, including firewall configuration, object versioning, static websites, and Python automation.
Secure Google Cloud compute from boot to runtime with keyless least-privilege identities, centralized SSH without public IPs, boot-integrity verification, and memory-level encryption.
Deploy solutions on Google Kubernetes Engine: build, schedule, load balance and monitor workloads, set up service discovery, role-based access control, security and persistent storage.
Containerize applications and run them on Google Kubernetes Engine: explore Google Cloud basics, container images with Cloud Build, Kubernetes architecture, Pods, and kubectl from Cloud Shell.
Learn to manage production containerized workloads on GKE, configure deployments with kubectl and YAML, apply autoscaling and monitoring, and secure multi-tenant clusters.
Manage Kubernetes Deployments, Jobs, and CronJobs on Google Kubernetes Engine, configure services and load balancers, and set up persistent storage with StatefulSets, ConfigMaps, and Secrets.
Secure GKE clusters with Kubernetes RBAC, IAM, Workload Identity and Pod Security Standards, monitor and log with Google Cloud Observability, connect Cloud SQL, and build CI/CD pipelines.
Introductory overview of cloud computing through Google Cloud’s interfaces and compute options for building applications.
Google Cloud (GCP) Platform: GCP Essentials, Cloud Computing, GCP Associate Cloud Engineer, Professional Cloud Architect
Create a Google Compute Engine virtual machine in the Cloud console and work through zones, regions, and machine types in a hands-on lab.
Deploy a monolithic application to a Google Kubernetes Engine cluster in the Google Cloud console, then break it apart into separate microservices.
Deploy a containerized application with Google Kubernetes Engine in a hands-on lab, creating and working with a managed cluster from the Google Cloud console.
Compare Cloud TPU accelerators, weigh their advantages and drawbacks against GPUs, and apply strategies for maximizing AI model performance, efficiency, and GPU/TPU interoperability.
Compare CPUs, GPUs, and TPUs for AI workloads, then select and optimize Google Cloud GPUs to accelerate model training and inference.
Create a Windows Server virtual machine in Google Compute Engine from the Google Cloud console and connect to it using RDP.
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