Detailed Design of Scalable Data Infrastructure for AI/ML
The detailed design of a scalable data infrastructure for AI/ML encompasses five key layers: data ingestion, storage, processing, feature store, and model serving. The ingestion layer handles real-time events and batch data, ensuring reliable delivery and schema compatibility. The storage layer utilizes low-cost object storage for raw data and a metadata catalog for organization. The processing layer employs distributed frameworks for ETL, ensuring data quality and lineage. The feature store provides a unified repository for machine learning features, while the model serving layer supports both low-latency online inference and high-throughput batch serving, facilitating efficient model deployment and operation.
This lesson details the five layers of the data infrastructure: ingestion, storage, processing, feature store, and model serving. We will examine their internal components, technology choices, and design considerations.
1. Data ingestion layer
The data ingestion layer handles two primary data categories using dedicated services:
Real-time events: High-velocity events (e.g., clicks, payments) are published to a distributed message queue like Apache Kafka. A stream processing engine (Apache Flink or Spark Structured Streaming) consumes, validates, and enriches these events before writing them to object storage. This supports real-time features and analytics.
Batch data: Large-volume data (e.g., database snapshots) arrives at scheduled intervals. A workflow orchestrator, such as Apache Airflow, manages the extraction, cleaning, and formatting of this data before loading it into the raw zone of the object storage. This ensures consistent datasets for training.
Both ingestion paths must ensure reliable delivery and schema compatibility. Tools like Confluent Schema Registry or validation libraries in Airflow prevent schema changes from breaking downstream systems.
To support lineage and debugging, all ingested data is tagged with metadata (e.g., timestamps, Kafka offsets, batch IDs). This ensures that both event streams and batch imports provide a dependable foundation for downstream processing.
Once data is reliably ingested, it must be durably stored and cataloged in the data storage layer.
2. Data storage layer
The data storage layer provides a scalable foundation for all ingested data. Incoming data lands in the raw zone of the data lake, typically hosted on low-cost object storage (e.g., Amazon S3, GCS). This zone preserves data in its original form, supporting schema-on-read flexibility.
Cleaned datasets move to processed zones, which use modern table formats to support ACID transactions, time travel, and schema evolution. For fast SQL analytics and BI dashboards, the system leverages a high-performance data ...