From Tokens to APIs: Deploying NLP and Language Models

Moving natural language processing models from a local environment to a reliable production system requires a solid understanding of both AI concepts and software engineering. This course guides you through the entire lifecycle of NLP deployment, starting from foundational text processing to scalable web services. You will transition from writing simple text scripts to architecting robust, production-ready APIs that serve language models. Through structured text-based lessons, you will learn how to handle text tokenization, manage model inference, and deploy your applications using modern containerization and API frameworks. What you'll learn: Understand foundational NLP concepts, tokenization, and how language models process text; Build high-performance web APIs for model inference using Python and FastAPI; Implement modern NLP pipelines using transformer libraries and pre-trained models; Configure vector storage and retrieval-augmented generation (RAG) basics for context-aware applications; Containerize your NLP applications with Docker for consistent deployments; Optimize API performance, manage model latency, and handle asynchronous requests. The course begins with core NLP terminology and tokenization basics before moving into hands-on API design and containerized deployment workflows. You will study clear written explanations, review production-grade code snippets, and complete practical exercises to solidify your engineering skills. This course is designed for aspiring AI engineers, software developers, and data scientists who want to deploy their models to production. Basic Python knowledge is recommended, but no prior NLP deployment experience is required. Start reading today to bridge the gap between machine learning models and real-world web applications.