This is a reference guide for designing, deploying, and maintaining production-ready machine learning systems, grounded in MLOps best practices. It covers the complete machine learning lifecycle, from system design and workflow planning through to deployment and ongoing maintenance, with a focus on reliability, scalability, and maintainability as business requirements evolve.
The guide provides an architecture reference for establishing shared ML infrastructure, including model registries and feature stores that standardize asset reuse across teams. It details pipeline automation through configurable directed acyclic graphs with automated triggers and retry logic, and describes a production monitoring framework for detecting performance degradation, data drift, and algorithmic bias in real time. Responsible AI implementation is addressed through built-in fairness checks and bias detection mechanisms that validate model outputs against ethical guidelines.
The material is organized around key architectural patterns such as DAG-based pipeline orchestration, infrastructure-as-code provisioning, and a pipeline-defined ML lifecycle with clear handoff points from data collection to production monitoring. It serves as a practical manual for planning end-to-end ML workflows and designing systems that stay reliable and maintainable over time.
