Embedding Quantization Using Sentence Transformers: Speed Up Retrievel & Reduce Latency and Cost.
In this video, you'll learn about embedding Quantization, a technique that can significantly improve the speed and efficiency of retrieval tasks using Sentence Transformers.
Struggling with slow retrieval times and high costs when working with large datasets of text embeddings? Embedding quantization can be your solution! By leveraging techniques like scalar and binary quantization, this approach can dramatically reduce the size of embeddings, leading to significant cuts in memory usage, storage space, and processing time. This translates to faster retrieval, lower latency, and reduced costs – especially when dealing with massive datasets. We'll explore both scalar and binary quantization methods, allowing you to choose the best approach for your specific needs.
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