Learn to implement an experimental KERAS Feature Space for advanced feature scaling in a medical AI system through a hands-on coding demonstration. Build a structured data classification model that analyzes multiple laboratory results to predict heart attack probabilities using COLAB. Master the implementation of coherent feature scaling techniques, construct an optimized ETL pipeline, and develop both training and inference models utilizing cloud-based GPUs or TPUs. Follow along with practical code examples using the official KERAS Jupyter notebook, while exploring the new keras.util.FeatureSpace functionality available in TensorFlow 2.12 development version. Progress through key concepts including health report analysis, data normalization, model training, and making predictions for virtual patients, all while understanding the technical limitations and proper usage contexts of this experimental feature.