Chapter 7: ML Infrastructure
Build production ML systems: model serving, experiment tracking, feature stores, data pipelines, monitoring, and end-to-end MLOps workflows.
Chapter Overview
ML Infrastructure is the discipline of building reliable, scalable systems that take machine learning models from notebooks to production and keep them performing well over time. While training a model may take days, operating it in production takes months or years — and the engineering challenges of production far exceed those of training.
This chapter covers the complete stack: how to serve models with low latency and high throughput, how to track experiments and manage model versions, how to build feature stores that prevent training-serving skew, how to orchestrate data pipelines that feed fresh data to your models, how to monitor deployed models for drift and degradation, and how to tie it all together with MLOps practices that automate the model lifecycle.
The central theme is that ML in production is a systems problem, not a modeling problem. The difference between a successful ML project and a failed one is rarely the model architecture — it is whether the team built the infrastructure to deploy it reliably, monitor it continuously, and update it efficiently when the world changes.
Chapter Roadmap
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Model Serving Overview
Batch vs real-time, the latency-throughput trade-off, and the framework landscape for putting models behind APIs.
Lightweight Python services and dedicated model servers
FastAPI for ML
Pydantic schemas, async endpoints, file uploads and dependency injection for production-grade Python services.
TorchServe & Triton
MAR archives, dynamic batching, ensembles and multi-model serving on dedicated inference servers.
GPU Management
CUDA, memory hierarchies, multi-GPU strategies and the optimisation tricks that fit big models on smaller cards.
Tracking experiments and reusing features
Experiment Tracking
Runs, metrics, hyperparameters and artifact storage that make ML reproducible and comparable.
Feature Stores
Online and offline stores, point-in-time joins and the feature reuse layer that prevents training-serving skew.
Pipelines and live monitoring
Data Pipelines
DAG orchestration, idempotent tasks, validation, dataset versioning and backfill strategies.
Model Monitoring
Data drift, prediction drift, performance tracking, A/B testing and retraining triggers.
End-to-End MLOps
CI/CD, model registries, pipeline orchestration, automated retraining, IaC and governance — the full loop.
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