Efficient Model Deployment with BentoML (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"Efficient Model Deployment with BentoML" "Efficient Model Deployment with BentoML" is an in-depth guide written for machine learning engineers, DevOps professionals, and MLOps architects aiming to master modern model deployment strategies. The book opens by charting the evolution of model deployment, contrasting traditional methods with scalable, cloud-native architectures, and highlighting the significant challenges in performance, maintainability, and compliance that accompany contemporary AI infrastructure. By addressing the convergence of DevOps and MLOps, the text establishes a solid fou...
"Efficient Model Deployment with BentoML" "Efficient Model Deployment with BentoML" is an in-depth guide written for machine learning engineers, DevOps professionals, and MLOps architects aiming to master modern model deployment strategies. The book opens by charting the evolution of model deployment, contrasting traditional methods with scalable, cloud-native architectures, and highlighting the significant challenges in performance, maintainability, and compliance that accompany contemporary AI infrastructure. By addressing the convergence of DevOps and MLOps, the text establishes a solid foundation for navigating today's rapidly shifting landscape of production AI systems. Delving into BentoML's architecture, the book meticulously explores its core concepts, system design patterns, extensibility, and integration with widely-used ML frameworks like TensorFlow and PyTorch. Readers learn how to construct robust, production-ready services, ensure reproducibility through dependency management, and uphold quality standards with automated testing and service versioning. Through detailed workflows and hands-on practices, the chapters equip practitioners to package, distribute, and manage advanced BentoML deployments - from single models to complex, multi-model pipelines - while leveraging best-in-class CI/CD practices and performance benchmarking techniques. Beyond the technical implementations, the book offers comprehensive guidance on scaling model serving, optimizing for high throughput and low latency, and integrating BentoML into enterprise environments via Kubernetes, workflow orchestrators, and legacy system extensions. Critical topics such as observability, monitoring, and governance are addressed alongside thorough coverage of security architectures-ensuring safe, auditable, and regulatory-compliant deployments. Concluding with forward-looking chapters on managed services and next-generation deployments at the edge and hybrid clouds, "Efficient Model Deployment with BentoML" serves as an indispensable reference for robust, enterprise-ready machine learning operations.
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