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Uptime

As explained in Machine Learning System Design

The property that required data and related systems can be obtained or accessed reliably by a system and its engineers.

Availability depends on what must stay accessible. In a data pipeline, it means having data ready to feed the system before it runs. For a distributed data store, it means every request receives a valid response, though that response may not contain the latest data. section 6.6 section 16.2

When a network partition forces a choice, a system can cancel an operation to favor consistency or proceed to preserve availability, potentially returning stale data. Pipeline availability is also one of three criteria, alongside reproducibility and consistency. section 16.2 section 6.8

How Machine Learning System Design covers it

How deep it goes
Defines pipeline and distributed-store availability, then contrasts valid responses with guarantees of freshness.
Examples
Uses data that must be ready before a system runs and a network partition that forces a consistency-versus-availability choice.
What you can do after
Evaluate the tradeoff between returning a potentially stale response and canceling an operation to favor consistency.
How it connects
Connects availability to data pipelines, consistency, reproducibility, the CAP theorem, and partition tolerance.

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This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.