🔮 Inference Explained Like You're 5

Using trained models to make predictions Day 83 of 149 👉 Full deep-dive with code examples The Exam Analogy After months of studying (training), you take the exam (inference). Training: Learning phase, intense, expensive Inference: Using what you learned, quick, efficient You use your knowledge to answer NEW questions you haven't seen! Training vs Inference Training Inference Learning Using Very slow Fast Needs lots of GPU Can use CPU Uses labeled data New unknown inputs Updates model Model is frozen Happens once Happens constantly How Inference Works # Training (done once, expensive) model = train ( millions_of_examples ) # Takes weeks save ( model ) # Inference (done constantly, fast) model = load ( " trained_model.h5 " ) result = model . predict ( new_input ) # Milliseconds! Real-World Inference Every time you use AI, that's inference: Ask ChatGPT a question → Inference Siri understands "Set a timer" → Inference Netflix recommends a movie → Inference Spam filter checks email → Infer