Foundation and architecture

System Architecture

Architectural Overview

The ML system follows a layered, service-oriented architecture designed to support independent scaling, fault isolation, and continuous evolution.

The major architectural layers include:

1. Data Sources External systems such as transactional databases, event streams, third-party APIs, and logs that generate raw data.

2. Data Ingestion Layer Responsible for reliably collecting data in batch or streaming modes. This layer ensures durability, ordering (where required), and schema validation.

3. Feature Engineering Layer Transforms raw data into meaningful, model-ready features using deterministic and versioned transformations.

4. Model Training Layer Executes offline training jobs using historical data and defined feature sets.

5. Model Registry Stores trained models along with metadata such as version, metrics, lineage, and approval status.

6. Inference Layer Serves models via real-time APIs or batch scoring jobs.

7. Monitoring and Feedback Loop Observes system health, model performance, and data quality, feeding signals back into retraining workflows

Design Rationale

Training and inference are physically and logically separated.

Models are treated as immutable artifacts.

All inter-service communication occurs through well-defined contracts.