  As applications in large organizations evolve, the machine learning (ML)
models that power them must adapt the same predictive tasks to newly arising
data modalities (e.g., a new video content launch in a social media application
requires existing text or image models to extend to video). To solve this
problem, organizations typically create ML pipelines from scratch. However,
this fails to utilize the domain expertise and data they have cultivated from
developing tasks for existing modalities. We demonstrate how organizational
resources, in the form of aggregate statistics, knowledge bases, and existing
services that operate over related tasks, enable teams to construct a common
feature space that connects new and existing data modalities. This allows teams
to apply methods for training data curation (e.g., weak supervision and label
propagation) and model training (e.g., forms of multi-modal learning) across
these different data modalities. We study how this use of organizational
resources composes at production scale in over 5 classification tasks at
Google, and demonstrate how it reduces the time needed to develop models for
new modalities from months to weeks to days.
