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Fan-out

Fan-out is a pattern in which one input or operation distributes work, messages, or tasks across multiple downstream branches or processing units.

How 2 Manning books explain it

Distributed Machine Learning Patterns

A fan-out pattern starts multiple separate workflow steps from a single upstream step so they can process the same input or output.

How it is used
Structures machine-learning workflows that launch independent training or serving branches from shared upstream data.
How deep it goes
Defines the branching pattern and works through a multi-model workflow, while deferring implementation details.
Examples
Uses YouTube-8M entity tagging with three training branches, two selected models, separate serving, and aggregated predictions.
What you can do after
Apply it to organize independent branches for training or serving within a complex machine-learning workflow.

Read how Distributed Machine Learning Patterns explains fan-out →

Learn Concurrent Programming with Go

A fan-out pattern distributes messages from one source among multiple concurrent consumers so that each message is processed by one consumer.

How it is used
Speeds pipeline work by load-balancing channel messages across concurrent goroutines.
How deep it goes
Defines shared-channel worker distribution and shows Go construction, but stops short of preserving message order.
Examples
Uses concurrent URL downloads, downloadPages workers, a shared input channel, and a coffee-shop queue analogy.
What you can do after
Build it from scratch by launching channel-reading workers and combining their outputs with fan-in.

Read how Learn Concurrent Programming with Go explains fan-out →

Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.