MLOps & Infrastructure · intermediate · concept 142 of 176

⚙️Hyperparameter Tuning

The knobs you set before training, learning rate, batch size, architecture width, decide whether a run converges or burns money. Tuning ranges from grid and random search to Bayesian optimization, and in deep learning the learning-rate schedule is almost always the knob that matters most. AutoML pushes the same search over whole models and features, and Neural Architecture Search applies it to the architecture itself.