У нас вы можете посмотреть бесплатно Navigating the Vector Database Landscape или скачать в максимальном доступном качестве, которое было загружено на ютуб. Для скачивания выберите вариант из формы ниже:
Если кнопки скачивания не
загрузились
НАЖМИТЕ ЗДЕСЬ или обновите страницу
Если возникают проблемы со скачиванием, пожалуйста напишите в поддержку по адресу внизу
страницы.
Спасибо за использование сервиса ClipSaver.ru
🚀 This episode of Gradient Dissent welcomes Edo Liberty, the mind behind Pinecone's revolutionary vector database technology. 🎙 Listen on Apple Podcasts : http://wandb.me/apple-podcasts As a former leader at Amazon AI Labs and Yahoo's New York lab, Edo Liberty's extensive background in AI research and development showcases the complexities behind vector databases and their essential role in enhancing AI's capabilities. Discover the pivotal moments and key decisions that have defined Pinecone's journey, learn about the different embedding strategies that are reshaping AI applications, and understand how Pinecone's success has had a profound impact on the technology landscape. ✅ Subscribe to Weights & Biases → https://bit.ly/45BCkYz ⏳Timestamps: 00:00 Introduction: Welcome and Episode Overview 04:36 Meet Edo Liberty: Background and Pinecone's Inception 09:12 What are Vector Databases? An Explainer 13:48 The Genesis of Pinecone: Founding Story 18:24 Challenges in Developing VDB Technology 23:00 Pinecone's Unique Approach to VDBs 27:36 Key Milestones and Successes 32:12 Exploring Different Embedding Strategies 36:48 Future Trends in AI and Database Technology 41:24 Leadership and Innovation: Edo's Philosophy 46:00 The Road Ahead for Pinecone and VDBs 50:36 Audience Q&A: Edo Answers Listener Questions 55:12 RAG Apps: Production Challenges & Wrap-Up 🎙 Get our podcasts on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google: http://wandb.me/gd_google YouTube: http://wandb.me/youtube Connect with Edo Liberty: / edo-liberty-4380164 / edoliberty Follow Weights & Biases: / weights_biases / wandb