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The MLOps Blueprint: Build Reliable Machine Learning Systems That Scale, Monitor, and Deliver Real Business Value.
Paperback
Publisher: Independently Published
Published: Nov 1 2025
Pages: 278
Weight: 1.07
Height: 0.58 Width: 7.00 Depth: 10.00
Language: English
Can your machine learning models survive the real world?
Most AI systems never make it past the prototype phase, not because the algorithms fail, but because the infrastructure around them does. From unreliable data pipelines to broken deployments and unseen model drift, the gap between an experiment and a production system is where most ML efforts collapse.
Inside, you'll learn how to:
- Build production-ready ML pipelines with reproducible data, code, and model versioning.
- Automate model training, testing, and deployment using robust CI/CD and continuous training (CT) frameworks.
- Deploy models with confidence using canary releases, blue-green deployments, and automated rollback mechanisms.
- Monitor models in production for performance, data drift, and fairness, all while linking metrics directly to business KPIs.
- Establish MLOps governance through traceability, compliance documentation, and secure access controls.
- Design scalable architectures that balance cost, latency, and reliability across cloud, on-prem, and edge environments.
If you've ever struggled to answer questions like Can we reproduce this model? or How do we measure business ROI from our ML system? this book provides the roadmap you need.
Turn your models into measurable, reliable value. Start building scalable, production-grade AI systems today with The MLOps Blueprint.
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