What is Distributed Computing
The use of multiple connected machines to run computation that would be too large for one system.
Definition
Distributed Computing is the use of multiple connected machines to run computation that would be too large for one system. In practical AI work, it helps teams connect a concept to data, model behavior, product choices and evaluation. The useful question is not only what the term means, but how it affects quality, cost, reliability and risk in a real workflow.
Example
A team uses Distributed Computing to choose a model, design an experiment, compare alternatives or check whether an AI tool fits the task.
Why it matters
Distributed Computing matters because infrastructure decisions shape speed, cost, reliability, security and what an AI product can do in production.
How it works
Teams define data flows, compute requirements and access patterns, then test whether the system stays reliable under load. For Distributed Computing, the key is to connect the definition with input data, assumptions, measurable outcomes and deployment limits.
Where it is used
- Used in model platforms, data systems, deployment pipelines, monitoring, search, retrieval and production AI services.
Limitations
Infrastructure choices can hide cost, latency, security and maintenance tradeoffs, so they must be tested in realistic conditions.