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Every day, hundreds of millions of new Tweets containing over 40 languages ofever-shifting vernacular flow through Twitter. Models that attempt to extractinsight from this firehose of information must face the torrential covariateshift that is endemic to the Twitter platform. While regularly-retrainedalgorithms can maintain performance in the face of this shift, fixed modelfeatures that fail to represent new trends and tokens can quickly become stale,resulting in performance degradation. To mitigate this problem we employlearned features, or embedding models, that can efficiently represent the mostrelevant aspects of a data distribution. Sharing these embedding models acrossteams can also reduce redundancy and multiplicatively increase cross-teammodeling productivity. In this paper, we detail the commoditized tools,algorithms and pipelines that we have developed and are developing at Twitterto regularly generate high quality, up-to-date embeddings and share thembroadly across the company.

Fighting Redundancy and Model Decay with Embeddings