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.
~12 focused hours·intermediate
Tools: Cloud Run, AWS Lambda/ECS, Azure Container Apps, env config/secrets manager, HTTPS/TLS
Market relevance — share of job ads asking for this
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
Practice
Deploy a Public AI Endpoint with Logs and Rollback
Deploy the containerized ticket/RAG API to Cloud Run (or Lambda/Azure Container Apps), with env-based secrets, a /health endpoint, autoscaling min/max set, and structured logging. Simulate a bad deploy and practice rolling back to the previous revision, documenting the exact steps.
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
- Public HTTPS URL of the deployed service (or a screen recording if later torn down for cost)
- Deployment config (cloudbuild.yaml/GitHub Action) in a public repo
- Logs/screenshot showing the rollback from a broken revision to a working one
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?