"BentoML: Packaging, Deploying, and Monitoring ML/LLM Services End-to-End" Modern inference systems demand more than a model server: they require reproducible packaging, disciplined deployment workflows, scalable runtime behavior, and production-grade observability. This book is written for experienced machine learning engineers, platform engineers, MLOps practitioners, and backend developers who need to run ML and LLM services with the rigor of real software systems. It approaches BentoML not as a narrow tool, but as a unified platform for building and operating reliable inference applications end to end. Readers will learn how to design durable inference APIs, author class-based services, package deterministic runtime environments, and produce versioned Bento artifacts that move cleanly through deployment pipelines. The book also examines BentoCloud operations, revision management, rollback strategy, service composition, long-running task endpoints, OpenAI-compatible LLM serving, and performance tuning across CPU and GPU workloads. By the end, readers will be able to reason clearly about artifact identity, deployment semantics, concurrency, scaling, monitoring, and the trade-offs that distinguish robust production systems from fragile demos. Structured as a progressive, implementation-oriented guide, the book connects architecture, packaging, deployment, and operations without repeating concepts across chapters. It is best suited to readers already comfortable with Python, APIs, containers, and production infrastructure, and it rewards those who want deep operational u