Machine Learning at Scale: Building Enterprise-Grade Systems for Real-Time Inference, Distributed Training, and Continuous Operations
Jun 2026 · ramakanth evani
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About this ebook
Machine Learning at Scale explores the architecture, infrastructure, and operational practices required to build and manage enterprise-grade machine learning systems. The book covers real-time feature engineering, distributed training, model serving, MLOps, observability, vector databases, security, and cloud-scale deployment strategies, providing practical guidance for engineers designing reliable and scalable ML platforms.
Machine Learning at Scale explores the architecture, infrastructure, and operational practices required to build and manage enterprise-grade machine learning systems. The book covers real-time feature engineering, distributed training, model serving, MLOps, observability, vector databases, securi...
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About the author
Ramakanth Evani, Eur Ing, is a Senior Software Engineer, Technical Lead, and AI researcher with over 18 years of experience designing scalable enterprise software, cloud-native platforms, and distributed systems. A Senior Member of IEEE, he has contributed to research, peer review, and technology innovation across financial services, healthcare, media, and enterprise software domains. His professional interests include machine learning systems, MLOps, cloud computing, and the practical deployment of artificial intelligence at scale.
Ramakanth Evani, Eur Ing, is a Senior Software Engineer, Technical Lead, and AI researcher with over 18 years of experience designing scalable enterprise software, cloud-native platforms, and distributed systems. A Senior Member of IEEE, he has contributed to research, peer review, and technology...
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