Scalability is a significant challenge when working with embeddings, especially when dealing with large datasets or high-dimensional embedding spaces. As the number of items (e.g., documents, images, or users) increases, the computational cost of generating and comparing embeddings grows. Searching for similar items in a large embedding space can become computationally expensive, requiring specialized algorithms for efficient similarity search, like Approximate Nearest Neighbor (ANN) methods.
Another scalability issue is memory usage. Embedding models, particularly those with high dimensions, require a lot of memory to store the embeddings for all items. In cases where the dataset is enormous, storing the embeddings for every possible item in memory becomes infeasible. Techniques such as dimensionality reduction (e.g., PCA or UMAP) and distributed storage systems can help manage the memory requirements by reducing the dimensionality or spreading the embeddings across multiple machines.
Additionally, as embedding models are updated or retrained over time, ensuring that the new embeddings are seamlessly integrated into the system without causing significant downtime or performance degradation is essential. This requires careful design and efficient batch processing of embeddings. Scaling embeddings to work in real-time systems also requires optimization to ensure fast and accurate retrieval without overburdening computational resources.