Why Cloud for AI?
AI workloads need compute power that most local machines can't provide. Cloud platforms offer on-demand GPUs, scalable storage, and managed services that let you focus on building models instead of managing infrastructure.
Key Cloud Concepts
- Compute: VMs, containers (Docker), serverless functions
- Storage: Object storage (S3), databases (RDS), caches (Redis)
- Networking: VPCs, load balancers, API gateways
- GPU instances: For model training and large-scale inference
Containers and Docker
Docker containers package your AI application with all its dependencies into a portable unit. This ensures your model runs the same way in development, testing, and production — eliminating 'works on my machine' problems.
Key Takeaway
Cloud + containers = reproducible, scalable AI deployments. Learn Docker first, then cloud-specific services.