Feature Stores And Data Contracts

"A feature is not just a column; it is a promise made to a model."

Overview

Feature stores and data contracts make training-serving consistency explicit, testable, and deployable.

Production ML and MLOps are the mathematical discipline of keeping a learned system useful after it leaves the notebook. The model is only one artifact in a larger graph of data, code, configuration, evaluation, deployment, monitoring, and response actions.

This chapter uses LaTeX Markdown throughout. Inline mathematics uses $...$, and display equations use `

`. The central habit is to turn production behavior into explicit objects: versions, hashes, traces, thresholds, queues, contracts, and release decisions.

Prerequisites

• Full Dataset Assembly
• Data Versioning and Lineage
• Robustness and Distribution Shift
• Error Analysis and Ablations

Companion Notebooks

Learning Objectives

After completing this section, you will be able to:

• Define production ML artifacts using mathematical notation
• Represent dependencies as auditable graphs and contracts
• Compute simple production statistics with synthetic data
• Separate offline evaluation from online monitoring
• Design release gates that combine quality, safety, latency, and cost
• Explain how versioning enables rollback and reproducibility
• Diagnose drift, skew, and production regressions
• Connect LLM traces to evaluations, guardrails, and retraining data
• Identify operational failure modes before they become incidents
• Build lightweight notebook simulations of production ML behavior

Study Flow

1. Read the pages in order and pause after each page to restate the main definition or theorem.
2. Run theory.ipynb when you want to check the formulas numerically.
3. Use exercises.ipynb after the reading path, not before it.
4. Return to this overview page when you need the chapter-level navigation.

Runnable Companions

• Theory notebook
• Exercises notebook