Reliable Machine Learning Applying SRE Principles to ML in Production

What it’s about

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.

The through-line

Who it’s for

An ML engineer, data scientist, SRE, or engineering leader who wants their machine learning systems to run reliably and deliver value in production.

The problem

ML models that work in development break, drift, or silently degrade once deployed in real systems. Feeling overwhelmed and unequipped to operate ML at scale, unsure how to prevent or diagnose failures.

The plan

1. Understand the ML lifecycle loop as a continuous, whole-system process.
2. Establish sound data management, feature, and training-data practices.
3. Evaluate model validity and quality before deploying, and build in fairness and privacy.
4. Build robust training and serving infrastructure with reversible deployment.
5. Monitor, observe, and respond to ML-specific incidents.

The payoff

ML systems that run consistently, robustly, and reliably in production. · Failures detected early and mitigated quickly through good monitoring and incident response. · Ethical, fair, and privacy-respecting ML that stakeholders trust.

See our guide

• Build AI Systems That Ship →
• AI Safety, Governance & Alignment Practice →

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