Reliable Machine Learning Applying SRE Principles to ML in Production

Reliable Machine Learning is a whole-system, not algorithm-centric, guide to making ML actually work in the real world. Written by veteran SRE and ML practitioners from Google and beyond, it treats ML systems as data-processing pipelines that must be built, deployed, monitored, and maintained with the same rigor as any critical production service. It walks through data management, feature and training data handling, model evaluation, fairness and ethics, training and serving infrastructure, monitoring and observability, continuous ML, incident response, and the organizational and product dynamics required to sustain ML at scale. Rather than teaching you how models learn, it teaches you how to keep them running reliably, safely, cost-effectively, and responsibly—filling the operational gap that most ML books ignore.

What it argues

A causal model in which ML design levers and operational practices (data management, evaluation, monitoring, serving, org design) shape psychological and behavioral states of teams and system behaviors, which in turn drive ML reliability, quality, and business outcomes.

Key ideas it contributes

• Data Management Quality — The rigor and correctness of data collection, processing, annotation, storage, and governance across the ML pipeline.
• Model Evaluation Rigor — The thoroughness of validity and quality checks performed before deploying a model.
• Monitoring and Observability — The extent of instrumentation and alerting on ML-specific production signals like drift and prediction quality.
• Serving Infrastructure Robustness — The reliability, scalability, and reversibility of prediction-serving infrastructure.
• Ethical and Fairness Practices — The degree to which fairness, privacy, and ethics are built into ML design and deployment.
• Organizational Alignment — The configuration of people, process, and reward structures to support ML adoption and integration.
• Team Operational Confidence — The psychological state of teams feeling equipped and prepared to operate ML systems in production.
• Incident Response Capability — The behavioral capacity to recognize, mitigate, and learn from ML-specific incidents effectively.