"Seldon Core for Kubernetes Model Deployment"
"Seldon Core for Kubernetes Model Deployment" offers an in-depth, practical guide to deploying and managing machine learning models on Kubernetes using the powerful open-source Seldon Core platform. Designed for ML engineers, MLOps practitioners, and platform architects, the book balances foundational concepts with hands-on technical detail. It begins by establishing the context for model deployment challenges, contrasting Seldon Core with other leading frameworks, and providing the essential Kubernetes knowledge needed for ML workloads.
The architecture and capabilities of Seldon Core are dissected in detail, from the inner workings of custom deployments, inference graphs, and extension points to traffic management and advanced deployment patterns. Readers are guided through installation strategies, security and compliance enforcement, resource optimization, and scalable serving of both traditional ML models and large transformer-based architectures. Practical advice is provided for packaging and testing models, integrating with workflow engines and feature stores, designing enterprise-grade observability pipelines, and ensuring resilient, cost-effective operations.
Enriched with real-world enterprise case studies, hybrid multi-cloud patterns, and forward-looking discussions on governance and future trends, this book is a definitive resource for production-grade model serving. Whether you are deploying your first model or scaling inference across teams and clouds, "Seldon Core for Kubernetes Model Deployment" equips you with the expertise and best practices to deliver robust, compliant, and high-performance ML solutions at scale.
