The performance of a machine learning model is directly tied to the quality and consistency of its input data. While previous chapters focused on compute-intensive training and serving, this one addresses the data systems required to support them. In production environments, a significant challenge is ensuring that the data used for model training is processed in the same way as the data used for real-time inference. Any discrepancy, known as training-serving skew, can silently degrade model performance.

This chapter provides a systematic approach to engineering data pipelines and management systems for production machine learning. We will move from the theoretical importance of data to the practical implementation of systems that provide versioned, consistent, and timely features to both training and serving workloads. The goal is to build a data foundation that ensures reproducibility and reliability.

You will learn to:

• Architect and implement a feature store to centralize feature logic and eliminate training-serving skew, ensuring that a feature vector $f(x_{train})$ is identical to $f(x_{serve})$.
• Design data pipelines for both batch processing and real-time feature computation, examining the trade-offs between latency and cost.
• Incorporate data versioning and lineage into your ML workflows using tools like DVC to make experiments reproducible and auditable.
• Use distributed frameworks such as Spark and Ray for processing datasets at a scale that exceeds the capacity of a single machine.
• Structure and govern large data repositories like data lakes to effectively support both analytics and machine learning applications.

By the end of this chapter, you will have the skills to construct the data backbone of a production AI platform and will apply these concepts by building a basic feature ingestion pipeline.