How to Deploy an AI Model to Production
End-to-end guide to taking your AI model from development to a reliable, scalable production deployment.
What You'll Learn
This advanced-level guide walks you through how to deploy an ai model to production step by step. Estimated time: 18 min.
Step 1: Prepare your model artifacts
Package your model weights, tokenizer, and configuration files into a reproducible deployment bundle with version tracking.
Step 2: Choose a serving framework
Select vLLM for high-throughput LLM serving, TGI for Hugging Face models, or Triton for multi-framework GPU inference.
Step 3: Containerize and configure
Create a Docker image with your model and serving framework, configuring resource limits, health checks, and environment variables.
Step 4: Set up autoscaling
Configure horizontal pod autoscaling based on GPU utilization, request queue depth, and latency thresholds.
Step 5: Implement monitoring and rollback
Add Prometheus metrics, Grafana dashboards, and automated rollback triggers for latency or error rate regressions.
Frequently Asked Questions
Should I use GPUs or CPUs for model serving?▾
GPUs for large language models and image models. CPUs work for small models, embeddings, and low-throughput scenarios. GPU serving is 10-50x faster for transformer models.
How do I handle model versioning?▾
Use a model registry like MLflow or Weights and Biases to track versions. Deploy with canary releases so you can compare new versions against baselines.
What latency should I target?▾
For interactive applications, aim for under 500ms time-to-first-token. Batch processing can tolerate higher latency. Always measure p99, not just average.