The operational practices for running AI systems in production — lifecycle management, observability, drift detection, incident response, capacity planning, cost management.
AI Ops (sometimes 'AIOps' or 'MLOps' in adjacent contexts) is the operational practice for running AI systems in production. It covers: model lifecycle management (training, evaluation, deployment, retirement), observability (workflow-level metrics, model-level metrics, integration metrics), drift detection (data drift, concept drift, performance drift), incident response (AI-specific incidents like accuracy regressions or prompt-injection events), capacity planning, and cost management.
AI Ops is the discipline that keeps AI deployments healthy after the initial deployment. Without it, AI systems tend to degrade — input data shifts, model behavior drifts, integration changes break workflows, and cost grows unbounded. With it, AI deployments are observable, predictable, and continuously improved.
Production enterprise AI without AI Ops typically degrades over weeks or months. Customers without internal AI Ops capacity benefit from managed AI offerings that handle the AI Ops layer.
Drift detection on customer-uploaded documents to flag when input distribution shifts materially
Workflow-level observability dashboards showing throughput, exception rate, and accuracy over time
Incident response playbooks for AI-specific incidents (accuracy regression, hallucination spike, prompt-injection event)
Cost dashboards tracking inference cost per workflow class against revenue or business value
Beth includes AI Ops as part of the managed infrastructure — drift detection, observability, incident response, capacity planning, and cost management are operated by Huper. Customer-side observability dashboards provide visibility into workflow health without requiring the customer to operate the underlying AI Ops capability.
No. Beth includes AI Ops as part of the managed infrastructure. Customer-side roles managing Beth are typically operations leads, IT integration engineers, and (for higher-stakes deployments) the security and governance team — none of which need internal AI Ops expertise.
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