Yes, embeddings can be updated in real time, though doing so requires careful consideration of computational efficiency and system design. In real-time applications, such as recommendation systems or dynamic search engines, embeddings may need to be updated continuously based on user behavior or new incoming data. This is often achieved by retraining the model on a regular basis or fine-tuning the embeddings with new data as it becomes available.
Real-time updates may also involve incremental learning techniques, where the model updates its embeddings in small batches rather than retraining on the entire dataset. This allows the system to adapt quickly to changes without the need for full retraining, which would be resource-intensive. Techniques like online learning or adaptive learning rates can help embeddings evolve over time based on new data while avoiding the computational overhead of large-scale retraining.
However, updating embeddings in real time can be challenging in production systems, particularly when dealing with large datasets or high-dimensional embeddings. Careful architecture and optimization are necessary to handle frequent updates efficiently and ensure that the embeddings remain accurate and relevant.