Daniel Svonava is the Co-Founder of Superlinked. Daniel Svonava attended the Faculty of Informatics and Information Technologies at the 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.

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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