Today, we’re joined by Fernando Rejon, Senior Infrastructure Engineer at Zeta Alpha Vector, and Jakub Zavrel, Founder and CEO of Zeta Alpha Vector. In addition, they discuss MLOps for neural search applications data engineering, and how this innovation is pushing the bounds of search engines. In this episode, they explore how they use modern deep learning techniques to build an AI research navigator at Zeta Alpha. They engage in an in-depth discussion based on the challenges with setting up MLOps systems for neural search applications, how to evaluate the quality of embedding-based retrieval, progress and numerous pertinent criteria, contrasting the trade-offs of using in neural (information retrieval) search, and the trade-off with using it in practice and theory to standard information retrieval strategies. Additionally, they put into perspective the most important components you would need to build a POC neural search application. examine neural search models in both the retrieval and ranking phases from the perspective of scalability and predictability. They also outline conditions under which state-of-the-art results can be obtained. They also discuss the enormous work necessary to build and deploy neural search applications, which necessitates the use of greater processing resources, such as GPUs rather than CPUs, to get desirable output.