Understanding the formation and evolution of the Milky Way and its constituents relies on making measurements of data that are faithful representations of the underlying physical system. Data-driven spectral models function in several capacities in this context: generating spectra, extracting encoded information, and connecting that information to theory. I review how data-driven methods have changed the spectroscopic landscape and assess their future: ▪ A core utility of data-driven models is that they deliver precise stellar measurements at scale and with modest computational cost, providing consistent parameters and abundances across surveys, including for low-resolution and low signal-to-noise spectra. This circumvents prior limitations and unlocks the statistical power of large datasets. ▪ Entirely new avenues of study have been opened up by inferred spectroscopic ages, distances, and evolutionary states, whereas residual analyses have uncovered nonstellar signals and allow a novel discovery space. ▪ Biases can be introduced or inherited by data-driven models; identifying and accounting for them is important for accurate interpretation. ▪ Data-driven models do not replace theoretical models; together, they enable the interpretation and parameterization of spectra. ▪ A key benefit of data-driven frameworks, alongside public releases of survey data, is democratization of spectroscopic analysis. ▪ Future facilities and computational tools will expand data-driven methods and their utility. Although this review focuses on the stellar temperature range of 3,000–7,000 K, the methods are general.
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