Bridging the Gap Between Batch Processing and the Lakehouse for Next-Gen Customer Experience
//Abstract
As Generative AI (GenAI) and large language models (LLMs) evolve at an unprecedented pace, traditional machine learning architectures that rely on batch processing and static can no longer keep up with the amount of data they need to process. To beat competitors, numerous organizations are implementing real-time data streaming solutions, leveraging technologies like Apache Kafka and Apache Flink. These tools work together to ingest and process data in real-time, which, when combined with a vector database, can significantly boost the performance and reliability of GenAI applications. In this talk, we’ll dive into the benefits of the "shift-left" paradigm, which is all about moving from the old-school batch and lakehouse models to real-time data products. This shift allows companies to create GenAI applications that are more responsive and context-aware. By integrating streaming data with real-time model inference and using the Retrieval Augmented Generation (RAG) method, companies can cut down on latency and ensure their LLMs deliver up-to-date responses. We’ll cover key architectural patterns, potential challenges, and best practices for making this transition, all while sharing real-world examples of how integrating Kafka and Flink with vector databases can lead to next-level NLP applications.
//Bio
Pushkar is a Machine Learning and Artificial Engineer working as a Team Lead at Clari in the San Francisco Bay Area. He has more than a decade of experience working in the field of Engineering.
Pushkar's specialization lies around building ML Models and building Platforms for training and deploying models.
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8 окт 2024
