Best practices for managing embedding updates include establishing a strategy for periodic model retraining, monitoring performance, and using techniques like incremental learning. Embedding models should be updated when new data is available or when performance degrades over time. This can be done through scheduled retraining, where the model is trained with new data periodically, or by fine-tuning the model with incremental updates as new data arrives.
One common practice is to version the embeddings, storing the model weights and embeddings for different time periods or datasets. This allows for easy rollback to previous versions if necessary. In real-time systems, online learning techniques can be employed to update embeddings dynamically based on new interactions or data. For example, user embeddings in a recommendation system may be updated after each user interaction to provide more personalized results.
It's important to test the impact of embedding updates on downstream applications (e.g., recommendation quality or search relevance) and monitor performance over time to ensure that the updates lead to improvements. Additionally, version control and documentation should be used to keep track of changes and ensure reproducibility of the embeddings.