On this episode of the ML Platform Podcast, Frank Liu discusses the basics, problems and challenges of vector databases, including indexing strategies, segmentation, vector lengths used in production, GPU-accelerated vector databases, potential use cases, and more.
Timestamps:
00:00 Introduction
01:18 Who is Frank?
02:18 Vector databases 101
11:49 Embedding vector lengths used in production
13:27 Indexing strategies for vector databases
20:57 Vector updates and segmentation
27:43 The problem of updating embeddings and re-indexing
33:37 Milvus Lite, Milvus Standalone, and Milvus Cluster
35:09 GPU-accelerated vector databases
40:18 When to consider adding a vector database to your tech stack
47:44 Combining filtering with vector search
01:00:00 Combining keyword and vector search
01:04:36 Building a documentation chatbot with a vector database
01:09:14 Closing remarks
Resources:
ImageBind: Holistic AI Learning Across Six Modalities: ai.meta.com/blog/imagebind-six-modalities-binding-ai/
Frank’s website: frankliucs.com/
Milvus: milvus.io/
OSS Chat: osschat.io/
Follow us & stay updated:
► Vist our website: neptune.ai/?
► Follow us on Linkedin: www.linkedin.com/company/neptuneai/
► Follow us on Twitter/X: twitter.com/neptune_ai
► Check our Github: github.com/neptune-ai
Connect with Piotr on Linkedin: www.linkedin.com/in/piotrniedzwiedz
Connect with Aurimas on Linkedin: www.linkedin.com/in/aurimas-griciunas
Connect with Frank on Linkedin: www.linkedin.com/in/fzliu/
Connect with Frank on Twitter/X: twitter.com/frankzliu
The episode was recorded on 7 September 2023, and some information may not be up-to-date.
#vectordatabases #milvus #mlops #ml #llms
Негізгі бет Ғылым және технология Navigating Vector Databases: Indexing Strategies, GPU, and More
Пікірлер