Google Cloud Run
Deploy
Deploy a containerised service and inspect its configuration, logs and scaling behaviour.
All capabilities · Cloud, deployment & production
Deploy an AI service to the cloud
Ship an LLM/ML API on Cloud Run / AWS Lambda-ECS / Azure with HTTPS, env config and logs.
Explore 4 tools for this projectMarket relevance
Which roles ask for this — and how often
Share of job postings in India, per role, that name this capability.
What employers mean
You should be able to…
- Take a containerized API and deploy it to Cloud Run/Lambda/ECS with a public HTTPS URL
- Configure environment variables and secrets for a deployed service (not hardcoded)
- Set up autoscaling limits so a traffic spike doesn't blow the cloud bill
- Read and act on deployed service logs to debug a production error
- Roll back a bad deploy to the previous working version
- Set up a health-check endpoint the platform uses to know the service is alive
- Choose between serverless and container-based deploy for a given latency/cost profile
Needs first: Containerize an application with Docker
Learn — free, link-checked
The few resources that matter
Read · beginner · 40 min · docs.docker.com
Containerize a Python application
A Python-specific walkthrough for the exact Dockerfile patterns (deps, layers, entrypoint) you'll use to containerize an AI service. — Docker
Course · beginner · 90 min · cloudskillsboost.google
Google Cloud Skills Boost — Learning Paths
Free guided, hands-on labs (not just docs) for GCP and Vertex AI, with quizzes and a shareable badge for your portfolio. — Google Cloud
Read · intermediate · 30 min · cloud.google.com
Deploy a Python service to Cloud Run
The fastest free path from a Python API to a public HTTPS endpoint with autoscaling — the most common 'ship it' step in Indian AI job tests. — Google Cloud
Read · intermediate · 30 min · docs.aws.amazon.com
Building Lambda functions with Python
Covers the AWS alternative to Cloud Run — packaging, handlers and env config for a serverless Python deployment. — AWS
Read · intermediate · 30 min · learn.microsoft.com
Deploy your first container app
The Azure equivalent deploy path — useful since several Indian enterprise/BFSI employers standardize on Azure. — Microsoft Learn
Read · intermediate · 30 min · 12factor.net
The Twelve-Factor App
The config/state/logging principles behind every scalable cloud service, including the ones you'll deploy for AI inference. — Heroku / community
Tools for practice
Choose a tool for the job
Start with one tool for each part of your project. You don’t need to learn them all.
4 tools to explore
Amazon ECS
Deploy
Run containerised services and practise task definitions, networking and rollout checks.
Azure Container Apps
Deploy
Deploy a container application and configure its revisions, ingress and scaling.
Docker
Deploy · Build
Package a service with its dependencies and run a repeatable local environment.
Practices & references
- Configuration and secrets
- HTTPS/TLS
Practice
Deploy a public AI endpoint with logs and a practised rollback
Take your container image and put it behind a public HTTPS URL on a free tier that needs no card — a Render free web service or a Hugging Face Docker Space both deploy an image directly. Inject secrets through the platform's env/secret config rather than baking them into the image, add a /health endpoint that actually checks its dependencies, and turn on structured JSON logging. Then ship a deliberately broken revision, roll it back, and write down the exact steps and what the logs said on each side.
Start from
Milestones
- Get the image serving over public HTTPS with env-injected config · ~2.5h
- Add a /health endpoint that genuinely checks its dependencies · ~2h
- Switch to structured JSON logs and trace one request through the platform's log viewer · ~2.5h
- Deploy a broken revision on purpose, roll back, and capture logs from both sides · ~3.5h
Done when
- Service is reachable over public HTTPS and returns correct responses
- Secrets are injected via the platform's secret manager/env config, not the image
- /health endpoint returns 200 when dependencies are up and a non-200 when a dependency is down
- A documented rollback: deploy a broken revision on purpose, then roll back, with before/after logs
Prove it
Evidence a recruiter can check
- The rollback in three shots: the broken revision's error logs, the rollback action, and healthy logs after
- A live HTTPS URL (or a recording of one) returning real responses, with /health flipping non-200 when you stop its database
- The deploy config in the repo, showing every secret referenced by name and none committed
- One structured log line pulled from the platform's viewer, carrying request id, latency and status
Signal it
Deployed a containerized AI service behind public HTTPS with env-injected secrets, dependency-aware health checks and structured JSON logs — and rehearsed a rollback from a deliberately broken revision.
Interview
Questions you'll get asked
- Walk me through deploying a FastAPI service to Cloud Run from a Dockerfile.
- How do you manage secrets for a production deployment without putting them in code?
- A deployed service is returning 500s intermittently — how do you find out why?
- What's the difference between deploying to Lambda vs ECS vs a VM, and when would you pick each?
- How would you set up autoscaling so a viral traffic spike doesn't 10x your cloud bill?
- How do you roll back a bad deployment in under 5 minutes?
- What does your health-check endpoint verify, and why does that matter for uptime?