Reliable Machine Learning: Applying SRE Principles to ML in Production by Cathy Chen, Niall Richard Murphy, Kranti Parisa, D. Sculley, and Todd Underwood

The book shows data scientists, software engineers, SREs, product managers, and business owners how to run ML reliably, effectively, and accountably within an organization — from model monitoring in production to running a well-tuned model development team.

By applying an SRE mindset to machine learning, the authors and featured guest contributors work through the full ML lifecycle: data collection and analysis, training pipelines, build and validation, quality and performance evaluation, defining and measuring SLOs, launch, and monitoring and feedback loops.

Topics include:
• The ML lifecycle — from data collection through launch and feedback loops
• Data as liability — sensitivity, reliability, durability, version control, and privacy of ML data
• SLOs for ML — defining and measuring service-level objectives for models
• Monitoring and observability — catching model and data drift in production

408 pages, published by O'Reilly Media, October 2022.

Link: Book

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