Cloud Platforms
I build systems on AWS, Azure, and GCP, so these notes aren't introductions to any one provider. They're a record of what I've learned actually building and operating things on them, once a model or prototype has to handle real users, data, identity, networking, deployments, and cost.
This section holds three kinds of notes:
Platform-specific: things genuinely specific to one provider (services, APIs, identity model, deployment mechanics) and the odd provider-specific problem.
Cross-cloud: things that look similar across providers but aren't, like identity boundaries, private networking, observability, cost, and how workloads move (or don't) between clouds.
Architecture and decisions: the bigger calls, like managed services vs. containers, where to run inference, and how much portability is worth paying for.
Platforms
- AWS: the oldest and largest provider, with the widest catalog of services.
- Azure: Microsoft's cloud platform, tied closely to enterprise tooling and the wider Microsoft ecosystem.
- GCP: originated Kubernetes, strong in data analytics and ML infrastructure.
📄️ AWS
An overview of Amazon Web Services: how organizations, accounts, and regions are structured, and the core compute, storage, networking, database, and identity services.
📄️ Azure
An overview of Microsoft Azure: how subscriptions, resource groups, and regions are organized, and the core compute, storage, networking, database, and identity services.
📄️ GCP
An overview of Google Cloud Platform: how organizations, projects, and regions are structured, and the core compute, storage, database, and identity services, including its strengths in data analytics and ML.