Event‑Driven MLOps for Continuous Customization — ACM‑Style Technical Paper

Banner illustrating event-driven MLOps for continuous customization, featuring elements of streaming data, feedback loops, model orchestration, governance controls, and case studies for RAG assistants, contract extraction, and manufacturing QA.

Abstract

Modern AI systems evolve continuously: new data, changing policies, shifting user preferences, and updated tools demand continuous customization rather than episodic retrains. Event‑driven MLOps—the practice of driving data, model, and policy lifecycle transitions by events (data quality signals, policy changes, feedback, drift, incidents)—enables safe, auditable, and scalable evolution. This paper presents a practitioner’s blueprint for event‑driven MLOps across ingestion, labeling, training, evaluation, deployment, and governance. We formalize an event taxonomy, propose data and model contracts, specify streaming and pub/sub patterns, design feedback‑to‑learning loops, and define SLOs and guardrails (safety, fairness, latency, cost, and energy). We include implementation sketches, pseudo‑code, runbooks, case studies, and image/diagram prompts for architecture and dashboards.

CCS Concepts

•Computing methodologies → Machine learning; Online learning settings; Artificial intelligence;
•Information systems → Data management systems; Data stream mining;
•Software and its engineering → Software creation and management; DevOps;
•Computer systems organization → Cloud computing; Distributed architectures;
•Security and privacy → Software and application security; Access control.

Introduction

Static, batch‑oriented ML pipelines struggle in environments where inputs, policies, and objectives change weekly or daily. RAG assistants ingest fresh documents; extraction schemas evolve; agents acquire new tools and prompts; regulators update rules; seasonality shifts user intents. Event‑driven MLOps treats the platform as a responsive control system: signals trigger automated yet governed actions—retraining, adapter updates, index refreshes, rollout/rollback, cache invalidations, or policy revisions—while preserving auditability and SLOs.

Figure Prompt (Fig. 1 — Motivation): Three‑panel graphic: (a) static batch pipeline with lag, (b) volatile data/policy landscape (spikes, schema diffs), (c) event‑driven control loop (sense→decide→act) with governance.

Event Taxonomy and Contracts

Event Types Across the Lifecycle

•Data events: new sources onboarded, schema/version changes, CDC (change‑data‑capture), drift alerts (covariate/label/feature), quality rule violations, DLP hits, de‑duplication thresholds, PII reclassification, document recalls.
•Label/feedback events: human reviews, thumbs up/down, edit distances, rubric scores, disagreement spikes.
•Model events: new model/artifact registered, evaluation completed, canary regression, safety violation spike, energy/cost breach, fairness gap discovered, decay timers (TTL) reached.
•Policy events: policy ID updated, decoder/prompt changes, tool capability changes, tenant residency updates.
•Ops events: incident opened/resolved, capacity/latency SLO breach, cache invalidation, index checkpoint.

Figure Prompt (Fig. 2 — Event taxonomy wheel): Circular wheel segmented by Data, Label, Model, Policy, Ops; examples in each; arrows to “Actions”.

Contract Schemas

•Event envelope: {event_id, source, type, time, tenant, region, severity, payload_hash}.
•Data contract: schema version, nullable fields, units, allowed ranges, PII tags, provenance, SLAs.
•Model contract: task type, expected input/output schemas, safety/policy IDs, SLOs, supported locales.
•Policy contract: rule set IDs, allow/transform/block semantics, test suite version.

Figure Prompt (Fig. 3 — Contract registry): Catalog UI with versioned data/model/policy contracts, diff viewer, owners, and approval badges.

Reference Architecture

Control Plane

Event bus (pub/sub, log‑structured streaming) as the spine; workflow orchestrator (state machine) with idempotent handlers; policy engine and governance portal for approvals, audit, and rollbacks.

Data Plane

Ingest (CDC, object stores, APIs) → streaming validation (contracts, DLP) → feature/embedding pipelines → online/offline stores; RAG assets with incremental indexing, deduplication, provenance attestation.

Model & Evaluation Plane

Trainer services (SFT, adapters, preference/DPO, distillation), evaluator (faithfulness, rubric, safety), registry (models, prompts, policies), deployer (canary, A/B, shadow).

Figure Prompt (Fig. 4 — Event‑driven MLOps platform): Layered architecture: event bus center; surrounding rings for Data, Model, Policy, Ops handlers; arrows to stores and registries; governance on top.

Streaming Data and RAG Indexing

Ingest & Validation

Parse/validate against data contracts; route violations to quarantine; compute content hashes to dedup; run DLP/malware scans; extract metadata (jurisdiction, sensitivity, TTL, provenance).

Incremental Indexing

Delta indexing with tombstones; freshness SLOs; trust tiers (internal, vendor, web) with allow‑lists; chunking tuned to semantic boundaries; boilerplate removal; table normalization.

Index Governance

Attest sources; sign index snapshots; attach policy IDs and retention; support rollback to a prior snapshot via event.

Figure Prompt (Fig. 5 — Streaming indexer): Pipeline with stream validators → chunker → embedder → reranker features → index writer; checkpoint icons.

Feedback to Learning Loops

Feedback Signals

Explicit approvals/rubrics/edit diffs; implicit signals (click‑through, escalations, handle time, task success).

Learning Paths

1. Prompt & policy updates (low latency, test‑gated).
2. Adapter (PEFT) updates on curated diffs.
3. Preference optimization (DPO/RLAIF) on ranked edits.
4. Retrieval tuning via triplets mined from accept/reject logs.

on event output.accepted|edited as e:
  diff = compute_edit_distance(e.proposed, e.final)
  if diff > τ or e.rubric<4:
     enqueue(pref_queue, build_pair(e))
  update_metrics(e)

Figure Prompt (Fig. 6 — Feedback loop): Closed loop: Serve → Observe → Label/Rank → Train (adapters/DPO/reranker) → Evaluate → Deploy → Serve.

Orchestration Patterns

Idempotent Handlers & Sagas

Handlers are stateless with idempotency keys; complex sequences use sagas with compensating actions (e.g., roll back index snapshot).

Schedules vs. Signals

Hybrid control: cron windows for heavy jobs; event signals to trigger early or defer based on burn‑rates and SLOs.

Canary, Shadow, and A/B

Event model.registered triggers shadow; eval.completed triggers canary gated by safety/latency; kpis.ok transitions to A/B.

on model.registered(m): start_shadow(m)
if shadow.metrics pass gates: start_canary(m, pct=0.05)
if canary.kpis pass and no incidents: ab_test(m)
else: rollback(m)

Figure Prompt (Fig. 7 — Rollout state machine): States: Candidate → Shadow → Canary → A/B → Promote or Rollback; guards and events annotated.

SLOs, Error Budgets, and Guardrails

Latency (p95 by profile), availability, cost per accepted artifact, evidence coverage & entailment, safety violation rate, fairness deltas, energy per artifact. Burn‑rate alerts and degradation modes (smaller model, reduced k, evidence‑only, read‑only) triggered by events.

Figure Prompt (Fig. 8 — SLO dashboard): KPI tiles with gauges; alerts panel; drill‑downs by cohort and region.

Lineage, Audit and Compliance

Tamper‑evident logs capturing event → action → artifact; link outputs to evidence, versions, and policies. Audit packs (datasets, contracts, model/prompt/policy diffs, evaluation results, incidents, approvals). Residency/access: region‑scoped stores and caches; per‑tenant encryption keys; scoped secrets.

Figure Prompt (Fig. 9 — Lineage graph): Graph view: artifacts as nodes; events as edges; filters for time/tenant/policy.

Edge and Hybrid Deployments

Edge inference for low latency and data residency; cloud control plane for training/eval. Event gateways synchronize: model/adapter updates, index deltas, policy changes; reconcile on connectivity recovery.

Figure Prompt (Fig. 10 — Edge hybrid): Edge sites with local caches and adapters; central governance; sync arrows with conflict resolution.

Security Safety and Policy as Code

Ingress filters (Unicode normalization, PII redaction, malware scans), retrieval allow‑lists, instruction firewalls. Policy engine (allow/transform/block; budget guards); two‑person rule for destructive tools. Red‑team harness on schedule and incidents; evidence‑only mode as a switch.

Figure Prompt (Fig. 11 — Defense‑in‑depth): Layered controls around event bus with fail‑closed paths and incident toggles.

Implementation

Contracts & Events :

DataContract:
  id: string
  version: semver
  fields:
    - name: string
      type: {string,int,float,bool,timestamp}
      nullable: bool
      pii: {none, direct, quasi}
      unit: string
      allowed_range: [min,max]
  retention_days: int
  jurisdiction: [..]
ModelContract:
  id: string
  version: semver
  input_schema: ref(DataContract)
  output_schema: ref(DataContract)
  policies: [policy_id]
  slos: {latency_p95_ms, cost_per_artifact, safety_per_k}
{
  "event_id": "uuid",
  "type": "data.schema.changed|drift.alert|model.registered|policy.updated|incident.opened",
  "time": "2025-10-14T08:00:00Z",
  "tenant": "acme",
  "payload": {"diff": "..."}
}

Service Hooks :

on data.schema.changed(dc):
  if contracts.compatible(dc): update_transformers(dc)
  else: pause_ingest(dc.source); open_ticket(dc)
on drift.alert(signal):
  if signal.persistent and impact>τ: enqueue_training(job=adapter_refresh)
on policy.updated(p):
  run_policy_tests(p)
  if pass: deploy_policy(p) else rollback(p)

Figure Prompt (Fig. 12 — Event handlers): Sequence diagrams for handlers reacting to schema/drift/policy events; success vs. rollback paths.

Case Studies

Support RAG Assistant. Index freshness, policy.update, feedback edits → incremental indexing, prompt/policy hot‑swaps, adapter refreshes → deflection +9.2%, p95 latency −16%, violation rate <0.1%.

Contract Extraction. Schema changes and locale drift → schema‑aware retraining, locale adapters → F1 +7 pts, reviewer time −28%, cohort gaps ≤ 2 pts.

Manufacturing Vision+Text QA. Defect taxonomy updates, new tools → adapter training on annotated defects; tool gating; edge sync → detection +5.5 pts, p95 3.2 s, zero P1 incidents.

Figure Prompt (Fig. 13 — Before/after KPIs): Grouped bars per case for quality, latency, violations, and cost.

Checklists

Readiness

•Event bus with schemas & authZ; contracts registered; incremental indexer/DLP/provenance; evaluator service; golden/challenge sets; red‑team harness.

Operations

•Burn‑rate alerts & degradation ladders; canary/rollback runbooks; tamper‑evident logs & audit packs; cohort dashboards & fairness gates; energy/cost reporting.

Security & Compliance

•Tenant isolation; scoped secrets; egress controls; policy‑as‑code with tests; HITL for irreversible actions; residency & retention enforced; right‑to‑erasure workflows.

Open Questions amd Future Directions

Causal control policies; proof‑carrying events; unified real‑time causal estimators; carbon‑aware scheduling; autonomous MLOps agents with verifiable guardrails and HITL escalation.

Conclusion

Event‑driven MLOps turns ML platforms into adaptive, auditable control systems. By instrumenting lifecycle events, enforcing contracts, reacting with idempotent handlers, and gating with SLOs and policy‑as‑code, teams can deliver continuous customization—safer, faster, and cheaper—without sacrificing governance. The architectural patterns, schemas, pseudo‑code, and runbooks provided here offer a practical path from batch pipelines to responsive, production‑grade ML.

References

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