Three Pitfalls To Avoid With Embeddings` Embeddings are not static and require monitoring to ensure they continue to be meaningful over time. This includes tracking the loss of meaning as new concepts emerge in the real world, which can lead to a non-trivial problem. Monitoring involves setting up a point of comparison with the initial trained embedding and tracking metrics such as average distance between cluster centroids. Proper versioning of embeddings is essential to avoid heartache during iteration on code, while graphing techniques can provide an understanding of how well the embedding performs. Once in production, appropriate monitoring techniques are necessary to ensure consistent value for customers.