System Design: Data Infrastructure for AI/ML Systems

The rapid adoption of machine learning (ML) and artificial intelligence (AI) has created a critical need for scalable, reliable, and efficient data infrastructures. Designing a scalable data infrastructure for ML and AI systems focuses on building the underlying framework that enables data to be gathered, processed, stored, and served to ML models throughout their lifecycle.

This is not about building a single application. Instead, it focuses on architecting a foundational system that supports diverse ML workloads. These workloads include large-scale data ingestion and transformation, feature storage, and low-latency serving for inference.

Understanding the journey of data is just the first step. Next, we examine why building a dedicated data infrastructure is essential for AI/ML systems.

The need for data infrastructure for AI/ML

Modern ML systems are data-hungry. The quality and availability of data directly impact the performance of predictive models. A well-architected data infrastructure empowers data scientists and ML engineers to experiment (train and test), iterate, and deploy models faster and more reliably. It addresses common challenges such as data silosIsolated data in separate systems or teams, making it hard to access, combine, or share., inconsistent data qualityData that contains errors, missing values, duplicates, or conflicting formats, reducing its reliability for analysis or modeling., and the difficulty of reproducing experiments. The platform must ensure data consistency, high throughput, low latency, and scalability, empowering teams to develop and deploy intelligent systems at scale.

While such platforms offer numerous advantages, designing them is a challenging task. They must provide high throughput, low latency, data consistency, and high scalability to support diverse ML workloads. Many organizations struggle to leverage their data effectively because their existing infrastructure wasn't built with the unique demands of ML in mind. Let's examine why standard data architectures often fail to meet these needs.

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 simple 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 to handle the unique lifecycle of ML data. They often lead to several critical problems:

• Training-serving skew: Discrepancies between data used for training and data used in live predictions can degrade model performance.

• Lack of feature reusability: Without a centralized feature platform, teams often recreate the same features, wasting effort and creating inconsistencies.

• Reproducibility issues: Mutable or overwritten data make it hard to retrain models or reproduce experiments. Strict data and code versioning is essential.

• Scalability bottlenecks: Traditional systems struggle with computationally heavy transformations and aggregations as data volumes grow.