Serving Modern AI: An End-to-End Guide to Deploying Transformer Models with FastAPI and PyTorch

Note: This research paper was generated by Gemini Pro 2.5 and provides a comprehensive technical guide for ML deployment. Content has been adapted for blog format with assistance from Claude AI.

Abstract

#

This paper provides a comprehensive, practical, and conceptually grounded guide for deploying state-of-the-art Transformer models as production-ready services. It details the construction of a robust, high-performance API using a modern Python stack, specifically transformers==4.35.2, torch==2.1.0, numpy==1.26.1, fastapi==0.104.1, and uvicorn. The report begins by establishing the architectural rationale for this stack, followed by a foundational deep-dive into each component. It then presents a step-by-step walkthrough for building a complete inference API, addressing critical considerations from environment setup and efficient model loading to local testing and validation. Finally, the paper explores advanced strategies for production-grade scaling, latency optimization, and model compression, including knowledge distillation. The objective is to equip the reader—a first-year graduate student or junior ML engineer—with the necessary knowledge to bridge the gap between model development and real-world deployment.