Vector Databases Deep Dive

• Titel: Vector Databases Deep Dive
• Verlagsort: [Erscheinungsort nicht ermittelbar]
• Verlag: Packt Publishing
• Jahr: 2024
• Umfang: 1 online resource (1 video file)
• Fussnoten: Machine-generated record
• ISBN: 978-1-83702-667-8
• ISBN: 1-83702-667-X
• Datenträger: Online-Ressource
• Sprache: und
• Sach-SW: Réseaux neuronaux (Informatique)
• Sach-SW: Intelligence artificielle
• Sach-SW: artificial intelligence
• Form-SW: Instructional films
• Form-SW: Nonfiction films
• Form-SW: Internet videos
• Form-SW: Films de formation
• Form-SW: Films autres que de fiction
• Form-SW: Vidéos sur Internet
• K10plus-PPN: 1910475998
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4629524363
• Lokale URL Inst.: Zum Volltext

Abstract

Explore the dynamic world of vector databases, where modern data storage meets high-dimensional data management. This course begins by introducing the foundations of vector databases, explaining their purpose and how they differ from traditional databases. You'll uncover the reasons behind their growing popularity, diving into essential concepts like vectors, embeddings, and the complexities of high-dimensional data. Moving forward, the course covers advanced topics in search similarity and nearest neighbor algorithms, providing insight into efficient data retrieval in vector databases. You'll also examine various indexing strategies--including flat indexing, HNSW, and product quantization--enhanced by real-world illustrations that bring abstract concepts to life. This knowledge will enable you to choose the best indexing method for your needs. Finally, you'll apply these skills with hands-on introductions to leading platforms like Pinecone and Milvus, supported by practical demos. Closing with a forward-looking perspective, the course equips you with the expertise to navigate the evolving landscape of vector databases and implement them effectively in real-world scenarios. What you will learn Grasp vector database fundamentals and use cases Differentiate vector from traditional databases Apply advanced indexing and search methods Select optimal vector database solutions Implement vector databases in practical applications Analyze future trends in vector data storage Audience This course is ideal for data scientists, machine learning engineers, and database administrators looking to specialize in vector databases. A basic understanding of traditional databases and familiarity with vectors or embeddings is recommended, though the course provides a thorough introduction to key concepts. About the Author Babajide Ogunjobi: Babajide Ogunjobi is a seasoned data engineer and platform architect with over 15 years of experience designing and implementing scalable data solutions. His expertise spans major organizations like JP Morgan, Citigroup, and Publicis Sapient, as well as innovative startups such as Grubhub and Alphasense. Babajide specializes in building robust data platforms, pipelines, and warehouses that empower companies with data-driven insights and operational efficiency. Babajide's strategic approach and technical excellence have consistently delivered high-impact data infrastructures that align with complex industry needs, ensuring long-term performance and adaptability.