Model Serving

Why It Matters

Model serving is where your ML work becomes a product. You can have the most accurate model in the world, but if it can’t handle production traffic (fast responses, high availability, graceful scaling) it’s just an expensive experiment.

For AI engineers, model serving is often more challenging than model training. Training happens once; serving happens millions of times per day. Every millisecond of latency matters when you’re processing real-time requests. Every outage directly impacts users and revenue.

The complexity compounds with LLMs. These models are massive, require specialized hardware (GPUs), and have high per-request costs. Efficient serving becomes the difference between a viable product and an unprofitable one.

Implementation Basics

Model serving typically involves three layers:

1. Model Loading Your trained model needs to load into memory efficiently. For LLMs, this might mean loading quantized weights, using tensor parallelism across GPUs, or implementing speculative decoding. The goal: minimize time-to-first-token while maximizing throughput.

2. API Layer Wrap your model in an HTTP API. FastAPI is the standard for Python. Handle request validation, authentication, rate limiting, and response formatting. For LLMs, implement streaming responses so users see output as it generates.

3. Infrastructure Containerize with Docker, deploy to Kubernetes or cloud-managed services. Set up autoscaling based on request volume or GPU utilization. Implement health checks, load balancing, and graceful degradation.

Common serving frameworks: vLLM and TGI for LLMs, TensorFlow Serving and TorchServe for traditional ML, Triton Inference Server for multi-model deployments.

Start with managed services (AWS SageMaker, Google Vertex AI) to validate your product. Migrate to self-hosted when you need cost optimization or custom features at scale.