Distributed Systems
Systems composed of multiple networked computers that coordinate to achieve a common goal, appearing to end users as a single coherent system despite operating across many nodes.
Distributed systems spread computation and data across multiple machines to achieve scale, reliability, and performance that a single server cannot provide. Every modern cloud application is a distributed system, from web servers behind load balancers to databases with replication to microservice architectures communicating over networks.
The fundamental challenges are well-known: network unreliability (messages can be lost, delayed, or duplicated), partial failures (some nodes crash while others continue), clock skew (different servers have slightly different times), and consistency challenges (keeping data synchronized across nodes). These challenges make distributed systems harder to reason about and debug than single-machine programs.
For AI engineering teams, distributed systems knowledge is essential. Training large models requires distributed computation across GPU clusters. Serving predictions at scale requires load-balanced inference servers. Data pipelines process events across distributed message queues. Understanding concepts like consensus, replication, and fault tolerance helps teams build AI systems that are reliable under real-world conditions.
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