Register
Managing and serving data efficiently is critical to deploy successful AI applications at scale. Feature stores have emerged as an essential tool for organizations, centralizing and streamlining the management of data for AI/ML, from raw data ingestion to real-time model inference. This presentation will explore Feast, the open-source feature store designed to address common data challenges in the AI/ML lifecycle, such as training-serving skew, feature redundancy, and low-latency serving at scale.
We will dive into Feast’s core components, including its feature registry, offline store, and online store, and discuss how these components work together to enhance collaboration across data science and engineering teams. We will also cover real-world use cases, including real-time personalization, fraud detection, and Retrieval-Augmented Generation (RAG) for large language models. We will illustrate how Feast powers cutting-edge AI applications across industries. Whether you're an AI engineer or a data scientist, this talk will provide practical insights into how Feast can streamline your AI workflows and accelerate the delivery of production-grade AI solutions using open-source software.
Speaker
Francisco Javier Arceo
Senior Principal Software Engineer @Red Hat | Kubeflow Steering Committee Member | Feast Maintainer
Francisco Javier Arceo has spent over a decade working in AI/ML, software, and fintech at AIG, the Commonwealth Bank of Australia, Goldman Sachs, Fast, Affirm, and Red Hat in roles spanning software, data engineering, credit, fraud, data science, and machine learning. He holds graduate degrees in Economics & Statistics and Data Science & Machine Learning from Columbia University in the City of New York and Clemson University. He is a maintainer for Feast, the open source feature store and a Steering Committee member for Kubeflow, the open source ecosystem of Kubernetes components for AI/ML.
