GenAI for the People: The IT Guide to Scalable AI Infrastructure

Shezan Kazi

Head of Ai Transformation

Building core data and AiOps foundations

(For the architects, platform engineers, and heads-of-data  who have to make all this actually run.)

Ai transformation IT outline: 

I. Unified data layer

II. End-to-end AiOps stack

III. “Responsible-by-default” controls

IV. Quick-start checklist

V. Federated innovation model

Modern generative-Ai workloads lean on three pillars:

Continuous, automated governance

Rock-solid data architecture

An AiOps tool-chain that spans classic MLOps and LLM/Agent ops

Ship all three together or you’ll bottleneck every downstream use case.

Part I: Unified data layer

Establish a governed, interoperable data foundation—lakehouse, lineage, and vector access—so analytics, ML, and GenAI workloads draw from consistent, well‑controlled sources.

Lakehouse ≥ warehouse:
Adopt an open-table-format lakehouse (Delta, Iceberg, Hudi) so analytics, feature engineering, and vectorization share one source of truth.
Column- & row-level lineage
Emit metadata (OpenLineage, Marquez) from every pipeline—ETL, ELT, or streaming—so you can answer “Which prompt used what data?” in seconds.
Vector store as a first-class citizen
Persist embeddings in Postgres/pgvector, Weaviate, or Pinecone and sync them with the lakehouse via CDC jobs. Treat vector indexes like tables: versioned, access-controlled, auditable.
Dataclean-room pattern for sensitive corpora
Mask or tokenize in a quarantined zone, generate embeddings there, then expose only vectors to your LLMs.

Part II: End-to-end AiOps stack

Provide an end‑to‑end engineering pipeline—version control, CI/CD, orchestration, model/prompt management, serving, and feedback—to move Ai workloads from development into reliable operation.

Layer	What it solves	Battle-tested options
Source-control	Versioning of code and prompts	Git, DVC, 🤗 Hub
CI/CD	Automated unit + integration tests (model, prompt, RAG)	GitHub Actions, GitLab CI, Jenkins
Orchestration	DAGs for data, training, eval, deployment	Airflow, Kubeflow, Metaflow
Feature / Vector store	Online & offline feature serving, similarity search	Feast, Tecton, pgvector, Pinecone
Model registry	Artifacts, tags, lineage, promotion gates	MLflow, BentoCloud, HuggingFace Inference Endpoints
Serving infra	Low-latency endpoints, autoscaling, cost caps	KServe, Sagemaker, Nvidia Triton
Inference firewall	Toxicity, PII, jailbreak detection	LlamaGuard, Prompt Armor, Azure Content Safety
Observability	Latency, cost, drift, hallucination rate	WhyLabs + LangKit, Arize Phoenix, Helicone
Feedback loop	Human & synthetic labels, RL(AI)F fine-tuning	HumanLoop, OpenAI Evals, PromptLayer

Tip: Treat prompts and chains (LangChain, LangGraph, Semantic Kernel) as immutable artifacts—hash them, test them, roll them forward with blue/green deploys, just like micro-services.

Part III: “Responsible-by-default” controls

Embed policy, risk management, and audit instrumentation directly into data and model pipelines so every Ai workload is deployed and monitored in line with regulatory and ethical requirements.

Policy-as-code
Gate every pipeline and endpoint through OPA or Conftest rules that reference your Responsible Ai principles (see Section II).
Dynamic risk tiers
Classify data and model outputs (low/medium/high) and auto-route high-risk generations to human review.
Shadow-evals
Run nightly canaries that replay live prompts against the last 3 model versions; regress if quality, bias, or cost drifts beyond SLOs.
Audit snapshots
Materialize quarterly “model cards” + “prompt cards” with datasets, hyper- params, eval scores, and incident tickets. This will make EU AI Act Annex VIII a tick-box exercise instead of a fire drill.

Part IV: Quick-start checklist

Use this abbreviated action list to stand up a functional, governed Ai platform quickly and create an initial operating baseline.

Spin up a lakehouse repo with table formats + lineage hooks. 
Stand up CI/CD that lints code and prompts, then runs automated RAG/LLM evals. 
Register every model and embedding index; require one-click rollback. 
Instrument serving layer for latency, tokens, cost, safety flags, semantic drift. 
Enforce policy-as-code gates before promotion; auto-publish model & prompt cards.

Lock these foundations in early so that every subsequent pilot—whether classic ML, a chat-bot, or a multi-tool agent—plugs into the same paved road instead of inventing its own one-off stack.

Part V: Federated innovation model

Domain teams understand their own edge-cases; a lightweight central Ai office just writes the rules.

1. Guardrails (one-time setup)

Policy-as-code for data tiers, retention, and human-review triggers
Audit logging of every prompt/response
Risk tiers (low / medium / high) baked into the release workflow

2. Shared toolbox

Pre-approved APIs and UI widgets
Prompt & policy templates in a central repo
Secure AI tool-chain (model registry + vector store + content filter)

3. Go-live hygiene

Vendor SaaS only for “green” data classes, after DPA check
GDPR / SOC 2 pack in the release checklist
Pre-launch red-team, runtime safety filters, post-launch drift monitors

4. Quick checklist

Domain teams build; central team governs
Reuse the component kit—don’t reinvent
Log everything by default
Block nothing without a sanctioned alternative

Ship these guardrails and everyone can prototype safely at startup speed!

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