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Community
How Metadata Lakes Empower Next-Gen AI/ML Applications
Metadata lakes are centralized repositories that store metadata from various sources, connecting data silos and addressing various challenges in RAG.
![Streamlining the Deployment of Enterprise GenAI Apps with Efficient Management of Unstructured Data](https://assets.zilliz.com/large_Streamlining_the_Deployment_of_Enterprise_Gen_AI_Apps_with_Efficient_Management_of_Unstructured_Data_8e246829a0.png)
Community
Streamlining the Deployment of Enterprise GenAI Apps with Efficient Management of Unstructured Data
Learn how to leverage the unstructured data platform provided by Aparavi and the Milvus vector database to build and deploy more scalable GenAI apps in production.
![Multilingual Narrative Tracking in the News](https://assets.zilliz.com/large_June_14_Multi_lingual_narrative_tracking_in_the_news_real_time_experiments_97247d0270.png)
Case Study
Multilingual Narrative Tracking in the News
Recapping Robert Caulk's meetup talk, discussing the need and ways to track different narratives of news articles, using embedding models, LLMs, and Milvus.
![Harnessing Product Quantization for Memory Efficiency in Vector Databases](https://assets.zilliz.com/large_May_27_Harnessing_Product_Quantization_for_Memory_Efficiency_in_Vector_Databases_e13a0a413c.png)
VectorDB 101
Harnessing Product Quantization for Memory Efficiency in Vector Databases
Exploring product quantization's intricacies and practical implementation through hands-on examples.