From 2e64a95c8d177fee8df2b2f4fe096a68f4ec7c89 Mon Sep 17 00:00:00 2001 From: davidmyriel Date: Tue, 3 Dec 2024 15:26:20 -0800 Subject: [PATCH] Update facial-recognition.md --- qdrant-landing/content/blog/facial-recognition.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/blog/facial-recognition.md b/qdrant-landing/content/blog/facial-recognition.md index e1f7c0434..a519a6f81 100644 --- a/qdrant-landing/content/blog/facial-recognition.md +++ b/qdrant-landing/content/blog/facial-recognition.md @@ -120,7 +120,7 @@ Qdrant stands out as a high-performance [**vector database**](/qdrant-vector-dat ## Lessons and Takeaways -Scalability poses challenges when working with large datasets, such as 20,000+ images. Optimizations like using [**quantization techniques**](/documentation/guides/quantization/) to reduce memory usage or precomputing average embeddings for clusters can significantly minimize storage and computational costs. These strategies ensure the system remains performant as the dataset grows. +Scalability poses challenges when working with large datasets, such as 20,000+ images. Consider optimizations like [**quantization**](/documentation/guides/quantization/) to reduce memory usage or precomputing average embeddings for clusters can significantly minimize storage and computational costs. These strategies ensure the system remains performant as the dataset grows. The potential real-world applications of this technology extend far beyond entertainment. Similar systems can be used in security applications for embedding-based facial recognition to secure access to buildings or devices.