This post, originally written for the NVIDIA Developer Blog, details offline, online, and online large-scale recommendation system architectures. With a focus on deployment, we use a building block framework, NVIDIA Merlin, and a real-time data layer, Redis, to construct examples of end-to-end recommendation systems.
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Introducing “Python Libraries You Might Not Know” (PLYMNK), a new series dedicated to raising awareness of awesome projects and their maintainers in the Python Ecosystem. Often, I’ve run across a library that I wondered - “how did I not hear of this before”? My goal is for PLYMNK to evoke this question from even the most seasoned Python developers.
To kick this off, I’m shouting out a library that is responsible for enabling many of the performant Python libraries every data scientist, machine learning engineer, and scientist use on a daily basis: CIBuildWheel.
Search capability is ingrained into our daily life. Arguments are commonly ended with the conclusion, “just google it”. Users have come to expect that nearly every application and website provide some type of search functionality. With effective search becoming ever-increasingly relevant (pun intended), finding new methods and architectures to improve search results is critical for architects and developers. Starting from the basics, this blog post will describe AI-powered search capabilities within Redis that utilize vector embeddings created by deep learning models.