Phase 6: Cloud Deployment in Client Environments · 55 min · Python · AWS · GCP

VPC Configuration & Network Isolation

Your AI system lives inside their network. Configure it properly.

What you will learn

Configure VPCs: subnets, route tables, internet gateways, NAT gateways, VPC peering
Set up security groups and network ACLs with least-privilege inbound/outbound rules
Deploy AI workloads in private subnets: no public IPs, NAT for outbound-only access
Connect client VPCs with deployment VPCs: peering, Transit Gateway
Navigate cloud provider differences: AWS VPC vs. GCP VPC vs. Azure VNet

Objective

What you'll learn

This lesson takes approximately 55 min. By the end, you will be able to:

Configure VPCs: subnets, route tables, internet gateways, NAT gateways, VPC peering
Set up security groups and network ACLs with least-privilege inbound/outbound rules
Deploy AI workloads in private subnets: no public IPs, NAT for outbound-only access
Connect client VPCs with deployment VPCs: peering, Transit Gateway
Navigate cloud provider differences: AWS VPC vs. GCP VPC vs. Azure VNet

The Problem

When you deploy AI systems inside a client's cloud environment, you're deploying into their VPC. You need to understand VPC architecture: subnets, route tables, security groups, NAT gateways, VPC peering, and network ACLs. Your network configuration determines whether the system can reach the model API, access databases, and comply with security policies.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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