🧠 Building AI on the Cloud
Build production AI on AWS Bedrock, GCP Vertex AI, and Azure AI Foundry — managed models, RAG, agents, guardrails, and governance.
0/9
1Free2🔒★🔒 Standard3🔒4🔒★🔒 Standard5🔒6🔒★🔒 Standard🎓🔒 Professional
Managed LLM APIs
Call frontier models through a single endpoint with zero GPUs to manage, keeping data inside your cloud's IAM, networking, and compliance boundary.
AWS Bedrock
One AWS API unlocks many model providers, plus Knowledge Bases for managed RAG, Agents for tool-using workflows, and Guardrails for content and PII policy.
Chapter review
Chapter 1 review — Managed models & Bedrock
Lab: Grounded on Bedrock · 10-question check
GCP Vertex AI
Google's Vertex AI pairs Gemini and a model garden with Vertex AI Search for grounded retrieval, Agent Builder for agents, and built-in embeddings and evaluation.
Azure AI Foundry
Bring Azure OpenAI into the Microsoft stack with "on your data" RAG, Content Safety guardrails, prompt flow orchestration, and private endpoints for network isolation.
Chapter review
Chapter 2 review — Vertex & Azure
Lab: Ground it on two clouds · 10-question check
RAG on the Cloud
Let managed knowledge bases and vector stores handle chunking, embedding, indexing, and retrieval, so you ship the app instead of maintaining a vector database.
Production & Governance
Ship cloud AI that survives production by routing, caching, and capping tokens for cost, locking down least-privilege IAM and private networking, and monitoring to stay reliable.
Chapter review
Chapter 3 review — RAG & production
Lab: Productionize managed RAG · 10-question check
Final exam
Building AI on the Cloud — comprehensive capstone
Hands-on capstone + course-wide exam · scored to a role level