Cloud MLOps Use Case

Enterprise MLOps Observability

Every model deployment, every prediction served, every drift detected—tracked with complete audit trails. Finally answer: "Why did this model's accuracy drop?"

Syntax Decimal SD.32.05
Cloud MLOps

Live MLOps Event Stream

STREAMING

Cloud MLOps Pipeline

Data
Data
Ingest and validate
->
Train
Train
Model training
->
Eval
Evaluate
Metrics and bias
->
Deploy
Deploy
Endpoint serving
->
Monitor
Monitor
Drift and alerts

MLOps Query Examples

Model Drift Analysis

Track feature and prediction drift over time to catch model degradation early.

SELECT model_id, feature_name,
drift_score, baseline_dist, current_dist
FROM events
WHERE event_id = 'mlops.drift.detected:1'
AND drift_score > 0.15

Deployment Audit Trail

Complete history of model deployments including rollbacks and traffic splits.

SELECT model_version, endpoint_id,
traffic_split, deployed_by, reason
FROM events
WHERE event_id = 'mlops.model.deployed:1'
AND endpoint = 'fraud-detection-prod'

Fairness Metrics

Monitor model fairness across protected attributes for bias detection.

SELECT model_id, protected_attr,
demographic_parity, equal_opportunity
FROM events
WHERE event_id = 'mlops.fairness.evaluated:1'
AND demographic_parity < 0.8

Cost and Resource Tracking

Full visibility into training costs, prediction volumes, and resource utilization.

SELECT pipeline_id, compute_hours,
gpu_type, estimated_cost, region
FROM events
WHERE event_id = 'mlops.training.completed:1'
AND timestamp > NOW() - INTERVAL '30d'

Without Event Model

ML systems fail silently

With Event Model

Complete MLOps transparency

Model degradation
"Accuracy dropped from 95% to 82%" — no root cause found
Compliance audit
"Show the training data lineage" — scattered across systems
Production incident
"Which model version caused this?" — deployment history unclear
Model degradation
mlops.drift.detected:1 -> feature="income", drift=0.34, cause="data_shift"
Compliance audit
mlops.data.lineage:1 -> sources=["bq://dataset"], transforms=["normalize","encode"]
Production incident
mlops.model.deployed:1 -> version="v2.3.1", rollback_from="v2.4.0", reason="latency"

Why Observable MLOps?

Lineage

Data Lineage

Track data from source to model predictions with complete provenance.

Version

Model Versioning

Full history of model versions, experiments, and deployment decisions.

Drift

Drift Detection

Catch feature and prediction drift before it impacts business metrics.

Fair

Fairness Monitoring

Continuous bias detection across protected attributes and demographics.

Cost

Cost Attribution

Track compute costs per model, team, and project for chargeback.

Gov

Governance Ready

Meet enterprise AI governance requirements with automated documentation.

Enterprise and Regulatory Compliance

Lock

SOC 2 Type II

Security, availability, and confidentiality controls

Health

HIPAA

Healthcare data protection and audit requirements

Gov

FedRAMP

Federal government cloud security standards

Globe

ISO 27001

Information security management certification

EU

EU AI Act

High-risk AI system documentation requirements

Card

Model Cards

Automated model documentation and transparency

Make Enterprise AI Observable

Build trust through complete MLOps transparency for your organization's AI systems.

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