Deploying workloads in GCP - Step-by-Step Execution

Process Flow - Deploying workloads

Write workload config

↓

Submit deployment command

↓

GCP validates config

↓

GCP provisions resources

↓

Workload starts running

↓

Monitor status & logs

↓

Update or delete workload as needed

This flow shows how you prepare, submit, and manage a workload deployment on GCP step-by-step.

Execution Sample

GCP

gcloud run deploy my-service --image=gcr.io/my-project/my-image --region=us-central1

Deploys a container image as a managed service on Google Cloud Run in the specified region.

Process Table

Step	Action	Input/Command	GCP Response	Result
1	Write workload config	Container image URL, service name	Config ready	Deployment prepared
2	Submit deployment command	gcloud run deploy my-service --image=gcr.io/my-project/my-image --region=us-central1	Command accepted	Deployment started
3	GCP validates config	Checks image and parameters	Validation successful	Ready to provision
4	GCP provisions resources	Allocates compute, networking	Resources allocated	Workload starting
5	Workload starts running	Container starts	Service is live	Workload available
6	Monitor status & logs	gcloud run services describe my-service	Shows status and logs	Confirm healthy operation
7	Update or delete workload	gcloud run deploy or gcloud run services delete my-service	Updates or deletes service	Workload managed
8	Exit	No further commands	No errors	Deployment complete

💡 Deployment completes when workload is live and monitored or deleted as needed.

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5	After Step 6	Final
Deployment Status	Not started	Prepared	Started	Validated	Provisioned	Running	Monitored	Complete
Workload State	None	Config ready	Command accepted	Validated	Resources allocated	Live	Healthy	Managed

Key Moments - 3 Insights

Why does the deployment stop if the config validation fails?

Because GCP checks the config before provisioning resources (see Step 3 in execution_table). If validation fails, deployment cannot continue safely.

What happens if the container image URL is incorrect?

During validation (Step 3), GCP will detect the invalid image URL and reject the deployment, preventing resource provisioning.

How do you know the workload is running successfully?

By monitoring status and logs (Step 6), you confirm the service is live and healthy before considering deployment complete.

Visual Quiz - 3 Questions

Look at the execution_table, what is the deployment status after Step 4?

A Running

B Validated

C Provisioned

D Prepared

💡 Hint

Check the 'Deployment Status' column in variable_tracker after Step 4.

At which step does GCP allocate compute and networking resources?

A Step 4

B Step 2

C Step 5

D Step 3

💡 Hint

Look at the 'Action' and 'Result' columns in execution_table for resource allocation.

If the container image URL is wrong, what will happen during deployment?

A Deployment proceeds and workload runs with errors

B Deployment fails at validation step

C Resources are provisioned but workload never starts

D Deployment completes but service is unhealthy

💡 Hint

Refer to the key moment about config validation and Step 3 in execution_table.

Concept Snapshot

Deploying workloads on GCP involves:
- Writing workload config (container image, service name)
- Running deployment command (gcloud run deploy)
- GCP validates config before provisioning
- Resources are allocated and workload starts
- Monitor status and logs to confirm success
- Update or delete workload as needed

Full Transcript

Deploying workloads on Google Cloud Platform starts with preparing your workload configuration, such as specifying the container image and service name. You then submit a deployment command using the gcloud CLI. GCP validates your configuration to ensure it is correct and safe to deploy. Once validated, GCP provisions the necessary resources like compute and networking. The workload container starts running, and you can monitor its status and logs to confirm it is healthy. Finally, you can update or delete the workload as needed to manage your deployment lifecycle.