Our desire to better understand quantum emergence drove the community's efforts in improving computing power and experimental instrumentation dramatically. However, the resulting increase in volume and complexity of data present new challenges. I will discuss how these challenges can be embraced and turned into opportunities by employing principled machine learning approaches. The rigorous framework for scientific understanding physicists enjoy through our celebrated tradition requires any machine learning essential to interpret any machine learning. I will discuss our recent results using machine learning approaches designed to be interpretable from the outset. Specifically, I will present discovering order parameters and its fluctuations in voluminous X-ray diffraction data and discovering signature correlations in quantum gas microscopy data as concrete examples.