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Uptime

As explained in Generative AI in Action

The property of a system remaining operational and accessible for a high proportion of time.

Uptime is the proportion of time a system remains operational and accessible. For generative AI deployments, high availability is a production goal shaped by deployment choices, infrastructure, and reliability measures. section 11.2 section 11.7

Support availability with measures suited to the deployment:

  • Plan redundancy, failover, and disaster recovery. section 11.7
  • Use load balancing and redundancy strategies to support scalable, available cloud deployments. section 11.2
  • Monitor system health and use automated testing to help maintain performance. section 11.2

Deployment approach affects how availability is managed. Cloud services offer scalability, while on-premise deployments provide more control but may scale more slowly; hybrid deployments combine these strengths with added management complexity. section 11.2

How Generative AI in Action covers it

How deep it goes
Links high availability to redundancy, failover, disaster recovery, load balancing, monitoring, and automated testing.
Examples
Uses cloud load balancing and redundancy, alongside failover and disaster-recovery planning, as availability measures.
What you can do after
Operate/configure deployments by planning redundancy, failover, disaster recovery, monitoring, and automated testing.
How it connects
Connects availability with deployment, scalability, reliability, monitoring, and disaster recovery.

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This explainer was generated by Manning's knowledge graph from the text of Generative AI in Action. The section links point to the parts of the book it draws on.