Adding Common Sense to Machine Learning with TensorFlow Lattice

Training-serving skew: The offline numbers may look great, but what if your model will be evaluated on a different or broader set of examples than those found in the training set? This phenomenon, more generally referred to as "dataset shift" or "distribution shift", happens all the time in real-world situations. Models are trained on a curated set of examples, or clicks on top-ranked recommendations, or a specific geographical region, and then applied to every user or use case. Curiosities and anomalies in your training and testing data become genuine and sustained loss patterns. Bad individual errors: Models are often judged by their worst behavior --- a single egregious outcome can damage the faith that important stakeholders have in the model and even cause serious reputational harm to your business or institution.