AI-Ready Data Platform Framework

Replace {{PLACEHOLDERS}} with your environment values, then deploy to your stack.

# AI-Ready Data Platform Framework

**Version:** {{FRAMEWORK_VERSION}}  
**Owner:** {{ML_PLATFORM_TEAM}} / {{DATA_PLATFORM_TEAM}}  
**Last Updated:** {{LAST_UPDATED_DATE}}  
**Organization:** {{ORGANIZATION_NAME}}

---

## Executive Summary

This framework defines a **reference architecture for AI-ready data platforms** at {{ORGANIZATION_NAME}}. It covers feature stores, vector pipelines, RAG (Retrieval-Augmented Generation) data layers, governance for LLM workloads, and MLOps integration - ensuring data infrastructure supports traditional ML, generative AI, and agentic applications with consistent quality, security, and observability.

**Design tenets:**

- **One platform, multiple AI workloads** - batch ML, real-time inference, RAG, fine-tuning share core data layers
- **Governance by default** - PII, prompt injection surfaces, and model inputs are classified and auditable
- **Reproducibility** - features, embeddings, and training datasets are versioned and lineage-tracked
- **Separation of concerns** - data platform owns pipelines; ML platform owns training/serving orchestration

---

## 1. Reference Architecture

```
┌──────────────────────────────────────────────────────────────────────────┐
│                         CONSUMPTION & APPLICATIONS                        │
│   Batch ML Apps │ Real-Time APIs │ LLM Apps │ Agents │ BI + Analytics    │
└───────────────────────────────┬──────────────────────────────────────────┘
                                │
┌───────────────────────────────┴──────────────────────────────────────────┐
│                         AI SERVING LAYER                                  │
│  Model Registry │ Feature Serving │ Vector DB │ Prompt Cache │ Gateway   │
│  (SageMaker /   │ (Feast /        │ (OpenSearch│ (Redis /     │ (Bedrock /│
│   MLflow)       │  Tecton)        │  Pinecone) │  ElastiCache)│  custom)  │
└───────────────────────────────┬──────────────────────────────────────────┘
                                │
┌───────────────────────────────┴──────────────────────────────────────────┐
│                         AI DATA PROCESSING                                │
│  Feature Engineering │ Embedding Pipelines │ RAG Index Builders │ Labeling│
│  (Spark / dbt)         │ (Batch + Stream)    │ (Chunk + Embed)    │ (Human│
│                        │                     │                    │ + LLM)│
└───────────────────────────────┬──────────────────────────────────────────┘
                                │
┌───────────────────────────────┴──────────────────────────────────────────┐
│                         CURATED DATA LAYER (Gold + AI Zones)              │
│  Entity Features │ Document Corpus │ Knowledge Graph │ Training Exports  │
└───────────────────────────────┬──────────────────────────────────────────┘
                                │
┌───────────────────────────────┴──────────────────────────────────────────┐
│                         LAKEHOUSE (Medallion)                             │
│  Bronze → Silver → Gold (+ AI-specific platinum zone)                     │
└───────────────────────────────┬──────────────────────────────────────────┘
                                │
┌───────────────────────────────┴──────────────────────────────────────────┐
│                         SOURCES                                           │
│  OLTP │ Events │ Documents │ APIs │ Unstructured (PDF, HTML, media)    │
└──────────────────────────────────────────────────────────────────────────┘
```

---

## 2. Platform Capabilities Map

| Capability | Purpose | Primary Components |
|------------|---------|-------------------|
| **Feature Store** | Consistent offline/online features for ML | {{FEATURE_STORE}} (Feast, SageMaker Feature Store, Tecton) |
| **Vector Pipeline** | Embed, index, refresh semantic search | {{EMBEDDING_MODEL}}, {{VECTOR_DB}}, batch + stream jobs |
| **RAG Data Layer** | Ground LLM responses in enterprise knowledge | Document lake, chunk store, metadata, retrieval API |
| **Training Data Platform** | Versioned datasets for ML and fine-tuning | DVC, LakeFS, or SageMaker datasets |
| **Model Registry** | Model artifacts, approvals, deployment | MLflow, SageMaker Model Registry |
| **LLM Gateway** | Unified API, routing, guardrails | {{LLM_GATEWAY}} (Bedrock, LiteLLM, custom) |
| **Governance** | Policy, lineage, access for AI data | Catalog, LF-Tags, AI risk assessment |

---

## 3. Data Zones for AI Workloads

Extend the medallion model with AI-specific zones:


// ... download full template for remaining code