MLServer Deployment and Operations: The Complete Guide for Developers and Engineers
William Smith
Jul 2025 · HiTeX Press
Ebook
250
Pages
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About this ebook
"MLServer Deployment and Operations"
"MLServer Deployment and Operations" is a thorough and expertly curated guide to deploying, operating, and optimizing machine learning model servers in production environments. The book opens with foundational concepts, outlining architectural paradigms for ML serving, comprehensive model lifecycle management, and streamlined deployment pipelines. Readers will gain practical insights into managing diverse inference workload patterns, versioning strategies, artifact organization, and crucial pipeline transition steps that take models seamlessly from experimentation to real-world application.
As the journey progresses, the book dives deep into deployment strategies and automation, including advanced CI/CD workflows, risk-mitigating release patterns like blue/green and canary deployments, and vital rollback and disaster recovery mechanisms. With a strong focus on enterprise-grade APIs and interfaces, it explores robust API engineering—from REST and gRPC protocol design to authentication, rate limiting, and dynamic model selection. Readers also learn to build resilient infrastructure and orchestration frameworks using containers, Kubernetes, serverless approaches, and hybrid edge/cloud patterns, all while optimizing resource allocation, autoscaling, and load balancing for maximum performance and reliability.
Operational excellence is at the heart of the text, with dedicated chapters on observability, performance monitoring, and security. Advanced guidance covers logging, metrics, alerting, SLOs, and AIOps-powered automated remediation for self-healing operations. Essential topics on securing ML workloads span threat modeling, privacy compliance, RBAC, vulnerability management, and defending against adversarial attacks—all within the context of evolving regulatory demands. The book culminates in advanced topics such as distributed and federated serving, global model synchronization, state management in inference systems, and detailed, real-world case studies. Together, these sections equip engineering teams, architects, and ML practitioners with the knowledge needed to deliver scalable, secure, and future-proof ML serving platforms for even the most demanding production landscapes.
"MLServer Deployment and Operations" is a thorough and expertly curated guide to deploying, operating, and optimizing machine learning model servers in production environments. The book opens with foundational concepts, outlining architectural paradigms for ML serving, comprehensive model lifecycle management, and streamlined deployment pipelines. Readers will gain practical insights into managing diverse inference workload patterns, versioning strategies, artifact organization, and crucial pipeline transition steps that take models seamlessly from experimentation to real-world application.
As the journey progresses, the book dives deep into deployment strategies and automation, including advanced CI/CD workflows, risk-mitigating release patterns like blue/green and canary deployments, and vital rollback and disaster recovery mechanisms. With a strong focus on enterprise-grade APIs and interfaces, it explores robust API engineering—from REST and gRPC protocol design to authentication, rate limiting, and dynamic model selection. Readers also learn to build resilient infrastructure and orchestration frameworks using containers, Kubernetes, serverless approaches, and hybrid edge/cloud patterns, all while optimizing resource allocation, autoscaling, and load balancing for maximum performance and reliability.
Operational excellence is at the heart of the text, with dedicated chapters on observability, performance monitoring, and security. Advanced guidance covers logging, metrics, alerting, SLOs, and AIOps-powered automated remediation for self-healing operations. Essential topics on securing ML workloads span threat modeling, privacy compliance, RBAC, vulnerability management, and defending against adversarial attacks—all within the context of evolving regulatory demands. The book culminates in advanced topics such as distributed and federated serving, global model synchronization, state management in inference systems, and detailed, real-world case studies. Together, these sections equip engineering teams, architects, and ML practitioners with the knowledge needed to deliver scalable, secure, and future-proof ML serving platforms for even the most demanding production landscapes.
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