Cloud Deployment for ML (AWS-First) · 50 min · AWS CloudWatch · GCP Vertex AI · Azure ML
Multi-Cloud Patterns & Cloud Ops
The concepts are portable; the console is not. Learn one cloud deeply and you can read the others.
Hiring signal: Cloud-portable thinking plus production ops (monitoring, rollback, security)
What you will learn
- Map AWS deployment patterns to GCP Vertex AI and Azure ML
- Apply least-privilege IAM and proper secrets management
- Monitor an endpoint with metrics, logs, and alarms
- Configure autoscaling and a rollback strategy
- Choose a cloud and recognize lock-in tradeoffs
The Problem
Job postings say "AWS or GCP or Azure," and candidates panic that they learned the wrong one. They didn't. The three big clouds are the same concepts with different names and consoles — if you understand storage, identity, compute, and managed ML on AWS, you can read the others in an afternoon. What actually separates a deployable engineer from a risky one isn't which cloud they know; it's whether they run services safely: least-privilege access, monitoring, and a rollback plan for when a deploy goes wrong.
This lesson makes your AWS knowledge portable and adds the operational layer.
The Concept
The same primitives, three vocabularies:
Your ML service needs to read training data from S3 and write model artifacts back. A teammate suggests using the account's root credentials in the app config. What's wrong with this and what should you do instead?
Least-privilege IAM: create a role with only the permissions the service needs — read from one bucket, write to another. If those credentials leak, the blast radius is limited to those buckets. Root credentials give access to billing, all services, all data — a single leak is catastrophic. This principle (scoped roles, not shared admin keys) is identical across AWS IAM, GCP IAM, and Azure RBAC.
Concept AWS GCP Azure
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Object storage S3 Cloud Storage (GCS) Blob Storage
Identity IAM IAM Entra ID + RBAC
Serverless fn Lambda Cloud Functions Azure Functions
Containers ECS/Fargate Cloud Run / GKE Container Apps / AKS
Managed ML SageMaker Vertex AI Azure ML
Monitoring CloudWatch Cloud Monitoring Azure Monitor
Learn the columns on one cloud; the rows transfer. The risk you're hired to manage is the same everywhere: access, observability, and recoverability.
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers Build It, Use It, Ship It, Evaluation, Exercises, Key Terms, Common Pitfalls, Interview Framing — plus a hands-on lab, quiz, and project artifact.
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