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@@ -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.