Information Retrieval & Relevance // Daniel Svonava // #214

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Feb 24, 2024

The podcast with Daniel Svonava discusses the use of vector embeddings in information retrieval, optimizing recommender systems with vector compute, customizing search vectors for relevance, and the efficiency of specialized models. It explores vector databases, deep learning-based retrieval challenges, and the transformative power of vector embeddings in diverse applications.

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Introduction

00:00 • 3min

Evolution of Product Idea and Pivot towards Recommendation Systems as a Service

03:20 • 6min

Enhancing Recommender Systems with Advanced Vector Compute

08:57 • 28min

Optimizing Search Vectors for Enhanced Relevance

37:06 • 7min

Efficiency and Speed in Data Processing with Specialized Models

44:16 • 4min

Exploring Vector Databases and Vendor Offerings

47:49 • 4min

Excitement and Challenges of Deep Learning-Based Retrieval and Vector Compute in Various Applications

51:33 • 4min

Daniel Svonava is the Co-Founder of Superlinked. Daniel Svonava attended the Faculty of Informatics and Information Technologies, Slovak University of Technology. MLOps podcast #214 with Daniel Svonava, CEO & Co-founder at Superlinked, Information Retrieval & Relevance: Vector Embeddings for Semantic Search // Abstract In today's information-rich world, the ability to retrieve relevant information effectively is essential. This lecture explores the transformative power of vector embeddings, revolutionizing information retrieval by capturing semantic meaning and context. We'll delve into: - The fundamental concepts of vector embeddings and their role in semantic search - Techniques for creating meaningful vector representations of text and data - Algorithmic approaches for efficient vector similarity search and retrieval - Practical strategies for applying vector embeddings in information retrieval systems // Bio Daniel is an entrepreneurial technologist with a 20 year career starting with competitive programming and web development in highschool, algorithm research and Google & IBM Research internships during university, first entrepreneurial steps with his own computational photography startup and a 6 year tenure as a tech lead for ML infrastructure at YouTube Ads, where his ad performance forecasting engine powers the purchase of $10B of ads per year. Presently, Daniel is a co-founder of Superlinked.com - a ML infrastructure startup that makes it easier to build information-retrieval heavy systems - from Recommender Engines to Enterprise-focused LLM apps. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Daniel on LinkedIn: https://www.linkedin.com/in/svonava/?originalSubdomain=ch Timestamps: [00:00] Daniel's preferred coffee [00:13] Takeaways [04:59] Please like, share, leave a review, and subscribe to our MLOps channels! [05:22] Recommender system pivot insights [08:49] RaaS Challenges and solutions [10:23] Vector Compute vs Traditional Compute [13:20] String conversion challenges [17:18] Vector Computation in Recommender Systems [20:55] RAG system setup overview [26:00] ETL and Vector embeddings [31:04] Fine-tuning embedding models RAG [36:10] Flattening data for Vectors [37:18] Vector compute control insights [47:48] Vector Hub database comparison [51:22] Vector database partnership strategy [52:47] Vector computation in ML [55:43] Wrap up