Customizing Reusable Frozen ML-Embeddings with Vespa

Organizations are increasingly utilizing deep-learned embeddings for search and recommendation applications, facing challenges in managing these embeddings efficiently in production. An emerging strategy to address these challenges is the use of frozen foundational embeddings, which are reusable and can be tailored for specific tasks, thereby reducing the complexity and cost typically associated with embedding lifecycle management. Vespa serves as a platform that supports the customization of these frozen embeddings, allowing developers to modify the query tower in two-tower embedding models while keeping the document tower frozen. This approach significantly reduces the computational and storage costs involved in maintaining multiple embedding models. By using frozen embeddings, developers can streamline the process of deploying and evaluating new models without the need to reprocess data, facilitating a more efficient and scalable system for handling large-scale datasets. The integration of Vespa enables the execution of advanced transformations using deep neural networks, enhancing the flexibility and personalization of applications. This approach not only simplifies the infrastructure required for managing embeddings but also allows for frequent model deployment and evaluations, thereby increasing the efficiency and effectiveness of machine learning operations in production environments.