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Grokking Modern System Design Interview

System Design: Data Infrastructure for AI/ML Systems

Define the requirements for scalable data infrastructure supporting AI/ML systems, overcoming challenges like training-serving skew. Learn to estimate storage, compute, and bandwidth resources for high-traffic workloads. Select foundational building blocks to ensure low latency and high throughput for real-time inference.

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Scalable and reliable data infrastructure is essential for modern machine learning (ML) and artificial intelligence (AI) systems. This infrastructure supports data collection, processing, storage, and serving across the ML life cycle.

Rather than a single application, it is a foundational system supporting diverse workloads, including large-scale ingestion, transformation, feature storage, and low-latency inference.

Overview of the data pipeline for AI/ML
Overview of the data pipeline for AI/ML

The need for data infrastructure for AI/ML

Modern ML systems rely on high-quality, available data. A robust infrastructure enables data scientists and engineers to experiment, iterate, and deploy models reliably. It solves challenges like data silosIsolated data in separate systems or teams, making it hard to access, combine, or share. and inconsistent data qualityData that contains errors, missing values, duplicates, or conflicting formats, reducing its reliability for analysis or modeling. while ensuring reproducibility. The platform must provide high throughput, low latency, and scalability.

Designing these platforms is difficult. Many organizations struggle because standard architectures cannot meet ML demands.

The challenges of traditional architectures

General-purpose data warehouses A central system that collects and stores integrated, historical data from multiple sources (like sales, marketing, CRM) for analysis, reporting, and business intelligence (BI) or data lakesA centralized repository that stores vast amounts of raw data in its native format (structured, semi-structured, unstructured) from diverse sources. are not designed for the ML data life cycle, leading to critical issues:

  • Training-serving skew: Discrepancies between training and live data degrade model performance.

  • Lack of feature reusability: Without a centralized platform, teams recreate features, causing waste and inconsistency.

  • Reproducibility issues: Mutable data complicates experiment reproduction. Strict data and code versioning is essential.

  • Scalability bottlenecks: Traditional systems struggle with computationally ...

Technical Quiz

1.

What is training-serving skew?

A.

The difference in time it takes to train a model versus serving a prediction.

B.

Using different server hardware (e.g., CPUs vs. GPUs) for training and serving.

C.

Discrepancies between the data or features used for training and for live predictions.

D.

When a model is trained on significantly more data than it is served.

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Requirements

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