ArchGen

System Block Diagram of an AI Data Processing Pipeline

Focus: Ingestion -> Feature Store -> Training -> Inference. Key areas: PostgreSQL, REST APIs, Kafka Connect.

Use this as a block diagram of the system when explaining architecture.

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Diagram caption: System Block Diagram of an AI Data Processing Pipeline (Ingestion -> Feature Store -> Training -> Inference) has 4 layers: Data Sources & Ingestion, Processing & Feature Engineering, Model Lifecycle, Serving & Feedback.

Prompt

Create a system block diagram of an AI data processing pipeline. Include data sources (databases, APIs, event streams), batch and streaming ingestion, a data lake/object store, ETL/data cleaning, feature engineering, feature store, model training, model registry, batch inference, real-time inference, and monitoring with feedback/retraining. Keep it high-level with 4–5 layers, 3–5 modules per layer, and specify 2–3 items, responsibilities, and technologies per module.
Highlights
  • Layer details · Data Sources & Ingestion: Modules include Data Sources, Batch Ingestion, Streaming Ingestion.
  • Module responsibilities · Data Sources & Ingestion / Data Sources: Provide raw data feeds; Define source schemas; Emit change events
  • Layer details · Processing & Feature Engineering: Modules include Data Lake / Object Storage, ETL / Data Cleaning, Feature Engineering.

Overview

System Block Diagram of an AI Data Processing Pipeline (Ingestion -> Feature Store -> Training -> Inference) has 4 layers: Data Sources & Ingestion, Processing & Feature Engineering, Model Lifecycle, Serving & Feedback.

Layer details

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  • Data Sources & Ingestion: Modules include Data Sources, Batch Ingestion, Streaming Ingestion.
  • Processing & Feature Engineering: Modules include Data Lake / Object Storage, ETL / Data Cleaning, Feature Engineering.
  • Model Lifecycle: Modules include Feature Store, Model Training, Model Registry.
  • Serving & Feedback: Modules include Batch Inference, Real-time Inference, Monitoring & Feedback.

Module responsibilities

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  • Data Sources & Ingestion / Data Sources: Provide raw data feeds; Define source schemas; Emit change events
  • Data Sources & Ingestion / Batch Ingestion: Load historical datasets; Enforce ingestion contracts; Stage data for processing
  • Data Sources & Ingestion / Streaming Ingestion: Ingest live events; Normalize stream formats; Route events to processing
  • Processing & Feature Engineering / Data Lake / Object Storage: Store immutable data; Serve downstream ETL; Enable cost-efficient storage
  • Processing & Feature Engineering / ETL / Data Cleaning: Transform raw data; Ensure data quality; Prepare analytics-ready tables
  • Processing & Feature Engineering / Feature Engineering: Create model features; Publish features to store; Track feature lineage
  • Model Lifecycle / Feature Store: Serve consistent features; Reduce training/serving skew; Manage feature reuse
  • Model Lifecycle / Model Training: Train candidate models; Track experiments; Evaluate metrics
  • Model Lifecycle / Model Registry: Promote validated models; Enable rollback; Publish deployment metadata
  • Serving & Feedback / Batch Inference: Generate batch predictions; Write results to warehouse; Backfill historical scores
  • Serving & Feedback / Real-time Inference: Serve online predictions; Handle traffic spikes; Enforce SLA latency
  • Serving & Feedback / Monitoring & Feedback: Detect model degradation; Trigger retraining signals; Close the feedback loop

Key flows

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  • Raw data flows from databases, APIs, and streams through batch or streaming ingestion into the data lake, where ETL cleans and standardizes it before features are computed and published to the feature store.
  • Training jobs pull offline features to build models, register approved versions, and deploy them to batch and real-time inference services.
  • Inference outputs and user feedback are monitored for drift and quality, feeding a retraining loop that updates the model registry.