Int-AIML-Internal — Data Architecture Pattern

Problem

An organization needs to securely ingest, validate, and process training datasets from internal applications, and serve machine learning model predictions to internal workloads, keeping all traffic fully isolated from the public internet.

Solution

Establish private data pipelines and training workflows located entirely within secure private subnets. Ingest internal datasets (via local ETL workflows, private APIs, or internal messaging/file shares) into a centralized private Data Lake or Feature Store. Train models in isolated compute environments and deploy them behind private endpoints or internal load balancers to serve real-time predictions to internal consumers.

Cloud Paradigm

Internal MLOps Pipelines, Private Inference Serving, Feature Store Architecture, VPC Service Controls, and Private Ingestion Pipelines.

Implementation Guidelines

Solution Flow

Data Ingress & Model Training Flow:

Internal Data Provider: An internal database, file share, or business application exposes training datasets.
Data Ingestion (ETL): Private ETL jobs or event consumers ingest the raw data and write it to the private Data Lake.
Data Prep & Feature Store: A processing job cleans the data, calculates features, and stores them in the private Feature Store.
Model Training: An isolated ML training pipeline (e.g., private managed ML training jobs) reads historical data from the Feature Store, trains the model, and registers the artifact in the Model Registry.

Inference Flow:

Internal Inference Client: A backend microservice or internal consumer initiates a synchronous prediction request.
Private API Gateway / Load Balancer: Routes the request securely within the internal VPC to the model serving container.
Inference Endpoint: The serving container runs the prediction, queries the Feature Store for contextual features, generates the prediction, and returns the result securely.

Additional Details

Ingestion Mechanisms:
- ETL / Batch: Used for bulk dataset extracts from databases or file shares on a scheduled basis.
- Streaming: Used for real-time feature updates using private message queues or event streams (e.g., Kafka).
Unified Feature Store: Maintain a centralized Feature Store accessible internally to ensure identical data features are used during training (offline) and inference (online), preventing training-serving skew.
Model Lifecycle Governance: Automatically version model artifacts and code configurations in a private Model Registry to support rolling updates and rollbacks.
Data Minimisation: Ensure internal datasets are stripped of highly sensitive or PII data prior to ingestion into the analytics data lake.

Security Controls

Network Isolation: All ML workloads, datastores, training compute (e.g., private training clusters), and serving endpoints must reside strictly within private subnets (VPCs/VNets). Expose internal APIs only through private load balancers or internal service meshes.
Authentication & Authorisation: Enforce IAM role authentication and authorization (e.g., IAM Roles for Service Accounts) to govern interactions between ingestion pipelines, storage (Data Lakes/Buckets), and ML compute. Enforce mTLS for internal inference calls.
Data Encryption: Enable encryption at rest (KMS keys) on all Data Lake buckets and Feature Stores. Enforce encryption in transit (TLS 1.2 or higher) for all internal file transfers and API calls.
Least Privilege Access: Ensure training compute environments have read-only access to specific training buckets, and write-only access to model registries.