Cloud Computing
On-demand access to computing resources (servers, storage, databases, AI services) over the internet. Cloud providers like AWS, Azure, and GCP offer scalable infrastructure without owning physical hardware.
Why It Matters
Cloud computing is how most organizations access AI — through cloud-based APIs and managed services. It eliminates the need for massive upfront hardware investments.
Example
Using AWS SageMaker to train a model on 100 GPUs for a week, then scaling down to 2 GPUs for serving — paying only for what you use.
Think of it like...
Like renting a car instead of buying one — you get what you need when you need it, without the maintenance, insurance, and upfront cost of ownership.
Related Terms
GPU
Graphics Processing Unit — originally designed for rendering graphics, GPUs excel at the parallel mathematical operations needed for training and running AI models. They are the primary hardware for modern AI.
Compute
The computational resources (processing power, memory, time) required to train or run AI models. Compute is measured in FLOPs (floating-point operations) and is a primary constraint and cost in AI development.
API
Application Programming Interface — a set of rules and protocols that allow different software applications to communicate with each other. In AI, APIs let developers integrate AI capabilities into their applications.
Model Serving
The infrastructure and process of deploying trained ML models to production where they can receive requests and return predictions in real time. It includes scaling, load balancing, and version management.