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System integration

As explained in Generative AI in Action

The process of connecting software components or systems so they operate together and exchange capabilities or data.

System integration connects generative AI models with existing systems and workflows so organizations can deploy them compatibly within established operations. Production integration is complex and time-consuming, requiring coordination with other teams and stakeholders. section 11.1

Production integration should account for:

  • Compatibility with existing systems and workflows. section 11.1
  • Operational reliability, including performance testing, redundancy, failover, disaster recovery, quotas, and retry logic. section 11.7
  • Security, compliance, cost management, observability, and human oversight. section 11.1 section 11.7
  • Collaboration with stakeholders to support smooth deployment and responsible operation. section 11.1

How Generative AI in Action covers it

How deep it goes
Frames integration as a production checklist concern and discusses compatibility and coordination without specifying an integration architecture or implementation.
Examples
Uses existing workflows, API management, quota controls, retry logic, MLflow, Traceloop, and Prompt flow as operational integration points.
What it assumes
Assumes established systems and workflows, while emphasizing compatibility, stakeholder coordination, and production operating requirements.
What you can do after
Operate/configure generative AI deployments that fit existing workflows while accounting for reliability, oversight, security, compliance, and cost.
How it connects
Connects system integration with production deployment, human-in-the-loop, security and compliance, observability, scalability, LLMOps, and MLOps.
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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.