A computing model in which a task is split across multiple networked machines that work in parallel, enabling processing at scales impossible on any single machine. Distributed computing is the infrastructure layer that makes modern AI training and large-scale data processing possible.
Also known as parallel computing, cluster computing, distributed systems
Distributed computing divides a computational task across multiple machines that each work on a portion of the problem simultaneously and communicate through a network. The approach is necessary when a task exceeds what a single machine can store in memory, requires more processing power than one machine can provide, or must complete within a time window that sequential processing cannot meet. Training a large neural network, processing a day’s worth of clickstream events, and serving millions of ad impressions per second are all distributed computing problems.
Modern distributed systems abstract most of this complexity from the applications running on top of them. Cloud platforms like AWS, Google Cloud, and Azure expose distributed computing resources as managed services: developers specify what they want to compute, and the platform handles task scheduling, data partitioning, fault recovery, and network coordination. Frameworks like Apache Spark distribute data processing workloads, while GPU clusters running CUDA distribute neural network training across many processors simultaneously.
Distributed systems introduce failure modes that sequential systems do not have. Network partitions can split a system. Individual machines can fail mid-computation. Results from different workers must be reconciled. The engineering discipline of building reliable distributed systems is substantial, which is why most agencies use managed cloud services rather than building their own distributed infrastructure from scratch.
Every AI tool a working ad agency uses, from the language model responding to a prompt to the bidding algorithm deciding which ad to serve, runs on distributed computing infrastructure. Understanding the basics of how that infrastructure works helps agencies evaluate vendor claims about speed and scale, scope infrastructure-intensive projects correctly, and have informed conversations with client IT teams about data architecture.
Scale requirements explain cost structures. AI model training is expensive because it is computation-intensive and requires distributing work across large GPU clusters. Serving those models at scale requires always-on distributed infrastructure that costs money whether or not it is heavily used. Agencies that understand distributed computing can have more substantive conversations with vendors about pricing, performance guarantees, and infrastructure constraints.
Data infrastructure determines what analysis is possible. A client whose data sits in a single-machine database can run the queries that database supports. A client whose data is in a distributed warehouse can run arbitrary analytical queries at any scale. Agencies recommending data infrastructure to clients need to understand what distributed versus centralized architectures enable and cost.
Real-time campaign systems depend on it. Serving personalized ad content to millions of users simultaneously, updating audience segments from live event streams, and running real-time bid optimization all require distributed systems capable of handling requests in parallel. Understanding this architecture helps agencies assess whether vendor real-time claims reflect the infrastructure that use case actually requires.
An agency is scoping a real-time audience segmentation system for a client that processes roughly 800 million clickstream events per day. The initial plan calls for a single-machine database. A back-of-envelope calculation shows that processing 800 million events in a six-hour overnight window requires approximately 37,000 inserts per second, which exceeds single-machine capacity by an order of magnitude. The agency revises the architecture to use a distributed stream processing system that fans the event load across multiple workers in parallel. The revised infrastructure estimate is four times higher, but it is the only architecture that actually meets the processing requirement. The calculation is done before any vendor contracts are signed.
The automations and agents module of the workshop teaches you how to build AI workflows that connect data to action at the scale agency campaigns require, including what the infrastructure beneath those workflows actually involves.