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Also known as: Model Inference, Online Serving
Deploying models to production endpoints for real-time or batch inference.
DESCRIPTION
Model serving exposes trained models via APIs or batch jobs to provide predictions in production. It emphasizes latency, scalability, availability, and observability. Platforms like TensorFlow Serving, KServe, Seldon, and SageMaker offer standardized deployment and autoscaling. Serving integrates with registries, feature stores, and monitoring for robust ML operations. It is critical for turning ML assets into business value.
ALTERNATIVE DEFINITIONS
TensorFlow Serving
TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments.
Focuses on production-grade performance and flexibility.
KServe
KServe provides standardized inferencing on Kubernetes to deploy scalable and portable ML model serving.
Highlights Kubernetes-native, standardized inference services.
AWS SageMaker
Use SageMaker hosting services to deploy models and get real-time inference from scalable endpoints.
Emphasizes managed hosting and scalable real-time endpoints.
RELATED TERMS
REST API gRPC KServe Seldon Core TensorFlow Serving Real-time Inference Model Deployment Autoscaling A/B Testing
COMPETING TERMS
KNOWLEDGE AREAS
MLOps AI Data Engineering Real-time & Streaming Data Infrastructure
TARGET AUDIENCE
MLOps Engineer AI Engineer Data Scientist Data Engineer DevOps Engineer
EXTERNAL SOURCES
- Wikipedia - Software deployment
- KServe - Kubernetes-native inference platform docs
- Seldon - Open-source ML serving on Kubernetes
- TensorFlow Serving - Production serving system overview
- AWS SageMaker - Managed real-time inference endpoints
- BentoML - Packaging and serving ML models
TOP QUESTIONS
Why is Model Serving critical?
It operationalizes models, delivering predictions to users and systems with reliability and performance tailored to business needs.
Category: Relevancy
What is the biggest misunderstanding about Model Serving?
That serving is only an API; it encompasses scaling, monitoring, security, and model governance.
Category: Misunderstanding
How is Model Serving managed in production?
Use registries, versioned deployments, autoscaling, observability, and policies for rollouts and rollbacks.
Category: Management
What risks exist in Model Serving?
Latency spikes, drifting performance, dependency outages, and security vulnerabilities require robust safeguards and monitoring.
Category: Risk
What are common mistakes in Model Serving?
Ignoring feature freshness, lacking SLA-backed scaling, and failing to track model versions degrade reliability.
Category: Mistakes
What alternatives compete with online Model Serving?
Batch Inference processes predictions offline, reducing latency requirements at the cost of freshness.
Category: Alternatives
What is a hot topic around Model Serving?
Serverless inference, multi-model endpoints, and model observability for explainability and drift detection are trending.
Category: Topic
How do you choose between REST and gRPC for Model Serving?
REST offers ubiquity; gRPC provides performance and streaming. Pick based on clients, latency, and payload needs.
Category: Opinion
Where should teams start with Model Serving?
Begin with a managed platform or KServe, define SLAs, add tracing and metric dashboards, and implement rollout policies.
Category: Requirements