Learn AI
Build your understanding from AI fundamentals to production AI systems.
Prefer a guided sequence? Explore Learning PathsCloud & AI Infrastructure
Where AI workloads actually run — cloud platforms, containers, and GPU infrastructure.
8 conceptsAWS & GenAI
AWS Bedrock provides managed access to multiple model families, while SageMaker supports hosting custom or fine-tuned models.
Intermediate · 4 minAzure & GenAI
Azure OpenAI Service provides enterprise-governed access to OpenAI models within an organization’s existing Azure environment.
Intermediate · 4 minGoogle Cloud & GenAI
Google Cloud’s Vertex AI platform provides managed access to Gemini and other models alongside MLOps tooling.
Intermediate · 4 minKubernetes for AI
Kubernetes runs and scales the containers behind an AI service — the API layer, gateway, and any self-hosted models.
Advanced · 5 minEKS & GenAI
Deploying an AI service on EKS means packaging it as a container, defining its resource limits, and fronting it with a load balancer and AI gateway.
Advanced · 4 minGPU Infrastructure for AI
Serving models at scale means managing GPU allocation, batching requests, and deciding between shared and dedicated capacity.
Advanced · 5 minModel Gateways in Production
Tools like LiteLLM, OpenRouter, and Portkey implement the gateway pattern in practice — one interface across many providers.
Intermediate · 4 minScaling AI Workloads
Scaling an AI workload means autoscaling request handling capacity while treating GPU-backed inference as a separate, more constrained resource.
Advanced · 5 min