Title: Hi friends,<p>We are building EVA, an AI-Relational database system with first-class support for deep learning models. Our goal with EVA is to create a platform that supports AI-powered multi-modal database applications operating on structured (tables, feature vectors, etc.) and unstructured data (videos, podcasts, pdf, etc.) with deep learning models. EVA comes with a wide range of models for analyzing unstructured data, including models for object detection, OCR, text summarization, audio speech recognition, and more.<p>The key feature of EVA is its AI-centric query optimizer. This optimizer is designed to speed up AI-powered applications using a collection of optimizations inspired by relational database systems. Two of the most important optimizations are:<p>+ Caching: EVA automatically reuses previous query results (e.g., inference results), eliminating redundant computation and saving you money on inference.<p>+ Predicate Reordering: EVA optimizes the order in which query predicates are evaluated (e.g., running faster, more selective deep learning models first), leading to faster queries.<p>Besides saving money spent on inference, EVA also makes it easier to write SQL queries to set up multi-modal AI pipelines. With EVA, you can quickly integrate your AI models into the database system and seamlessly query structured and unstructured data.<p>We are constantly working on improving EVA and would love to hear your feedback!
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