Keras Model Inference: Predicting on New Data with Python

You have successfully trained a neural network, but how do you actually use it to make real-world decisions? Knowing how to take a saved model and generate predictions on completely new, unseen data is the crucial final step of any machine learning workflow. This text-based course guides you through the entire inference pipeline. You will transition from training environments to practical application, learning how to safely load your saved Keras models and format raw incoming data so your model can process it correctly. What you'll learn: Learn the fundamental concepts of model inference and the lifecycle of a trained neural network; Load saved models securely using modern Keras file formats and best practices; Preprocess raw, real-world data to match the exact shape and scale required by your trained model; Generate single and batch predictions using Keras inference APIs; Handle common post-processing steps to convert raw model outputs into human-readable decisions; Practice troubleshooting shape mismatches and data pipeline inconsistencies through written exercises. You will start with core definitions and the mechanics of model storage, then progress to loading models and preparing input pipelines. Finally, you will explore practical scenarios for running batch predictions and interpreting the results. This course is designed for beginner Python developers and aspiring data scientists who understand the basics of neural networks but need to learn how to deploy and use their models on new data. No advanced machine learning experience is required. Start reading today to turn your trained neural networks into active decision-making tools.