AI/ML Operations

Model Serving, LLM Observability & Drift Detection

AI/ML Operations | Technical Operations Excellence

<1%
Hallucination Target
>90%
Task Completion
>98%
Tool Accuracy
<5%
Human Escalation

LLM Observability

Traditional observability measures infrastructure. LLM observability measures:

DimensionQuestion
BehaviorIs the model doing what we expect?
QualityAre outputs accurate, helpful, safe?
ReasoningIs the chain-of-thought sound?

Hallucination Detection

SelfCheckGPT

Sample multiple completions, check consistency. Inconsistent facts = hallucination.

LLM-as-Judge

Use another LLM to evaluate groundedness against retrieved context.

CLAP

Cross-Layer Attention Probing - classifier on model activations (open-source only).

Chain-of-Thought Monitoring

AspectQuestion
FaithfulnessDoes CoT reflect actual reasoning?
VerbosityIs reasoning externalized?
ReadabilityCan humans understand it?
NecessityIs CoT required for complexity?

CoT most relevant when task is difficult enough to externalize reasoning

Training Pipelines

StageReliability Practice
Data IngestSchema validation, drift checks
Feature StoreVersioning, consistency
TrainingCheckpointing, resource limits
EvalAutomated benchmarks, holdouts

Bot Performance Metrics

MetricTarget
Task Completion Rate>90%
Tool Call Accuracy>98%
Context Utilization>70%
Hallucination Rate<1%
Human Escalation<5%

Drift Detection

TypeWhat to Watch
Data DriftInput distribution shifts
Concept DriftRelationship changes
Model DriftPrediction quality decay

Monitor production predictions vs training distribution continuously

LLM Observability Platforms

PlatformStrengthOSS?
LangfuseTracing, evalsYes
Arize PhoenixRAG analysisYes
LangSmithLangChain nativeNo

Model Serving

  • Canary deploys: A/B test model versions
  • Shadow mode: Compare new vs old without impact
  • Circuit breakers: Fallback to cached/simpler model
  • GPU monitoring: Utilization, memory, thermals

Observe the Reasoning

AI reliability requires new observability primitives.