Model Serving
Model serving is the discipline of deploying trained machine learning models as reliable, low-latency services that applications and end users can query in real time. It covers the design of inference servers, batching strategies, hardware acceleration (GPU/CPU), API contracts, and the orchestration layer that routes traffic, scales replicas, and manages model versions. Practitioners work with frameworks such as NVIDIA Triton, TorchServe, Ray Serve, BentoML, and vLLM to bridge the gap between a trained model artifact and a production-grade endpoint.
As AI products move from prototypes to always-on services, companies need engineers who can expose models through stable, cost-efficient APIs without sacrificing latency or reliability. The surge in LLM adoption has made advanced serving techniques — continuous batching, KV-cache management, prefill/decode disaggregation — first-class engineering problems that directly affect user experience and infrastructure cost. Roles such as ML Engineer, MLOps Engineer, and AI Platform Engineer now routinely list model-serving expertise as a core requirement.
🎓 Courses
Deploying Machine Learning Models in Production (Course 4 of MLOps Specialization)
by Andrew Ng
Part of the DeepLearning.AI MLOps Specialization, this course covers deployment pipelines, serving infrastructure, progressive delivery, and monitoring — directly applicable to production model serving.
Model Serving — Made With ML (MLOps Course)
by Goku Mohandas
A free, hands-on module that walks through building a model serving layer with FastAPI and Ray Serve, including batching, versioning, and health checks.
Signature-Aware Model Serving from MLflow with Ray Serve
by Hugging Face team
A practical, free cookbook recipe that demonstrates end-to-end model serving using MLflow model signatures with Ray Serve, using open-source Hugging Face models.
Ray Serve: Scalable and Programmable Serving (Official Docs & Tutorials)
by Anyscale / Ray team
The authoritative reference for Ray Serve, including guides on autoscaling, model composition, FastAPI integration, and Kubernetes deployment — covering real production patterns.
BentoML Getting Started — Hello World
by BentoML team
A free, official quickstart that covers wrapping a Hugging Face model in a BentoML Service, running it locally, and packaging it into a deployable Docker image — the fastest on-ramp for model serving.
📖 Books
Machine Learning Model Serving Patterns and Best Practices
Md Johirul Islam · 2023
The only book specifically focused on model serving patterns. Covers deployment strategies, monitoring, accessibility, and real-world architecture patterns for ML models in production.
Machine Learning in Production: From Models to Products
Christian Kästner · 2025
Published by MIT Press in April 2025, this CMU-curriculum book covers the full lifecycle from a trained model to a production system, including serving, scaling, safety, and responsible AI. The online version is freely available.
🛠️ Tutorials & Guides
Deploying Hugging Face Models with BentoML
A practical, step-by-step blog post showing how to wrap a Hugging Face diffusion model with BentoML and serve it in production, including Docker packaging and cloud deployment.
Deploying OpenAI-compatible LLMs with vLLM and BentoML
Covers the increasingly common pattern of serving open-weight LLMs via an OpenAI-compatible API using vLLM as the inference engine and BentoML as the serving layer — directly applicable to 2025 LLM deployment workflows.
Model Serving Comparison: TensorFlow Serving vs TorchServe vs Triton Inference Server
A framework comparison article that maps the tradeoffs between the three dominant open-source serving runtimes, helping practitioners choose the right tool for their model type and infrastructure.
🏅 Certifications
Machine Learning Engineering for Production (MLOps) Specialization
DeepLearning.AI / Coursera · Paid (audit free)
A 4-course Coursera specialization by Andrew Ng that covers the full MLOps lifecycle including model serving. Completing it yields a shareable Coursera certificate recognised by hiring teams.
Learning resources last updated: June 18, 2026