Serving PyTorch Models: Inference and Prediction Pipelines

Transitioning a trained machine learning model from a research environment to a live application is a critical step in any AI workflow. This written course guides you through the foundational concepts of serving PyTorch models, ensuring your models can process real-world data and return accurate predictions efficiently. You will transition from understanding raw PyTorch checkpoints to building robust inference pipelines. By working through clear written explanations and structured code examples, you will learn how to handle data preprocessing, manage model states, and expose your models via lightweight web APIs. What you'll learn: Understand foundational model serving terminology, serialization concepts, and the lifecycle of a prediction request; Load PyTorch model checkpoints and state dictionaries correctly for inference mode; Preprocess input data, including images and structured text, to match expected model dimensions; Perform efficient inference, configure evaluation modes, and disable gradient calculations; Extract and interpret prediction probabilities, class labels, and model outputs; Build a lightweight REST API endpoint using FastAPI to serve your PyTorch models. The course begins with core definitions of inference and model serialization, then moves step-by-step through loading weights, processing inputs, and structuring a clean, production-ready prediction pipeline. This course is designed for beginners who have basic familiarity with Python and PyTorch and want to learn how to deploy their models. No advanced DevOps or cloud deployment experience is required. Start reading today to bridge the gap between model training and real-world application deployment.