Model Serving

Model serving is the final and most visible stage of the machine learning lifecycle. It involves operationalizing trained models—turning static artifacts into live, responsive systems that power AI applications.

Common model serving architectures include:

• REST or gRPC APIs: Used to send input data and receive predictions
• Containerized inference: Serving models inside Docker or Kubernetes environments
• Serverless endpoints: Managed infrastructure like AWS SageMaker or Azure ML
• LLM orchestration: Tools like LangChain, Semantic Kernel, or RAG pipelines

Challenges in model serving include:

• Latency and scaling under real-time load
• Security of endpoints and access credentials
• Versioning and rollback of models
• Ensuring consistent behavior across environments

Serving also requires strong observability—so organizations can monitor how models perform in production and respond to issues like drift or model abuse.

How PointGuard AI Helps
PointGuard integrates with model serving layers across cloud and hybrid stacks. It inspects real-time inputs and outputs, applies runtime defense policies, and links model behavior back to the AI inventory and governance controls—ensuring models are not just running, but running safely.
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Learn more: https://www.pointguardai.com/ai-runtime-defense

References:

TensorFlow: Introduction to Model Serving

Unify.ai: Understanding Model Serving