Hybrid Cloud
What hybrid cloud is, why it matters for AI workloads with data gravity and compliance constraints, and AWS hybrid options including FSx for …
Cloud-Computing
AI Deployment Models - SaaS, PaaS, IaaS, and Serverless
How the four cloud deployment models apply to AI workloads: when to use managed models, platform endpoints, GPU instances, or serverless …
Amazon EventBridge - Event-Driven AI Orchestration
Using Amazon EventBridge to connect AWS AI services, trigger pipelines from S3 events, and build loosely coupled multi-step workflows.
Amazon S3 - Object Storage for AI Pipelines
How Amazon S3 functions as the storage backbone for AI data pipelines: ingest, staging, output, and lifecycle management.
AWS AI Services vs Azure AI - Complete Comparison
A service-by-service map of AWS AI and ML services to their Azure AI equivalents, covering language models, speech, vision, and MLOps.
AWS AI Services vs Google Cloud AI - Complete Comparison
A service-by-service map of AWS AI and ML services to their Google Cloud equivalents, covering language models, speech, vision, and MLOps.
AWS Lambda for AI Pipelines
Serverless inference, event-driven processing, and integration patterns with Bedrock, SageMaker, and Step Functions. Cost optimization for …
AWS Step Functions - Workflow Orchestration for AI Pipelines
How Step Functions orchestrates multi-step AI workflows, handles retries and errors, and integrates with other AWS services - with practical …
AWS Step Functions vs Lambda Chains for AI Orchestration
When to use state machines vs direct invocation for AI workflows. Error handling, retry patterns, cost comparison, and visibility …
Serverless Computing
What serverless computing means, how Lambda, Fargate, and Step Functions fit AI workloads, and when serverless is and is not the right …
Well-Architected Framework
The cloud architecture review methodology used by AWS, Azure, and Google Cloud to evaluate workloads against proven best practices across …