ベクターDB比較は、VectorHubからの無料でオープンソースのツールです。ベクターデータベースを比較するためのものです。
ベクターDB比較を使用するには、比較したいベクターデータベースをアップロードし、比較メトリックスを選択するだけです。ツールはデータベースを分析し、詳細な比較レポートを作成します。
Vector DB Comparison 会社名: Superlinked 。
Vector DB Comparison Githubリンク: https://github.com/superlinked/VectorHub
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ソーシャルリスニング
Vector embeddings at the center of your data stack/ Daniel Svonava (Superlinked)
There are 37 databases that now support vector search - we counted (vdbs.superlinked.com)! Let’s talk about the emerging ecosystem around vector embeddings and why you might want to make your next analytics project vector-powered. Working with vectors requires ETL, ML modeling and domain expertise - a lot to cover in a short talk - my goal is to lay out the building blocks and give you pointers for diving deeper. And finally, I’ll introduce you to Superlinked - our open source compute framework for turning complex data into vectors and building vector powered software. Check it out at https://github.com/superlinked/superlinked
Information Retrieval & Relevance // Daniel Svonava // MLOps Podcast #214
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
Daniel Svonava - Superlinked
Superlinked is a compute framework for turning data into vector embeddings that maximize retrieval quality and control. Founder: Daniel Svonava Founder's LinkedIn: https://www.linkedin.com/in/svonava/ Open roles: Senior ML engineer, Senior DevOps engineer, Senior SWE For more info: https://superlinked.com
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