What is the difference between training and serving?

Training: teach model from data (expensive, batch). Serving: use model for predictions (fast, real-time). Different infrastructure needs. Serving must be reliable and fast.

How do I handle high traffic?

Horizontal scaling (more instances), load balancing, caching, model optimisation, batching. Cloud platforms handle scaling. Design for your expected traffic patterns.

What about serving LLMs?

Resource intensive: GPUs often required, high memory. Options: API providers (OpenAI, etc.), managed services (Bedrock, Vertex), self-hosted (vLLM, TGI). Most use APIs to start.

How do I monitor served models?

Track: latency, throughput, errors, resource usage. Also: prediction distributions, data drift, model performance. Alert on anomalies. Logging for debugging.

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Model Serving

The infrastructure and processes for deploying trained models to make predictions in production environments.

In-Depth Explanation

Model serving is the process of deploying trained ML models to production environments where they can receive requests and return predictions. It's the bridge between model development and real-world use.

Model serving components:

Model server: Hosts model, handles requests
API layer: Exposes prediction endpoints
Load balancing: Distributes traffic
Scaling: Handles demand changes
Monitoring: Tracks performance, drift

Serving approaches:

REST APIs (most common)
gRPC (high performance)
Batch prediction (scheduled)
Edge deployment (on-device)
Streaming (real-time)

Tools:

TensorFlow Serving
TorchServe
Triton (NVIDIA)
BentoML, MLflow
Cloud ML services

Business Context

Model serving turns ML models into usable services. Good serving infrastructure ensures reliability, performance, and scalability.

How Clever Ops Uses This

We deploy and manage ML model serving for Australian businesses, ensuring reliable, scalable, and monitored production AI systems.

Example Use Case

"Deploying a fraud detection model behind an API that scores transactions in real-time, handling thousands of requests per second with sub-100ms latency."