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Remove memory implications from Module 3
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@@ -15,11 +15,10 @@ Tackle the memory and performance challenges of production-scale multi-vector se
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## Today's path
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1. Memory Usage Implications and Solutions
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1. Multi-Stage Retrieval with Universal Query API
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2. Vector Quantization Techniques
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3. Pooling Techniques
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4. MUVERA
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5. Multi-Stage Retrieval with Universal Query API
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6. Evaluating Search Pipelines
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5. Evaluating Search Pipelines
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You'll master the optimization strategies needed to deploy multi-vector search at scale.
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@@ -1,7 +1,7 @@
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---
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title: "Evaluating Search Pipelines"
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description: Learn how to evaluate different search configurations in terms of cost, latency, and retrieval quality.
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weight: 6
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weight: 5
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---
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{{< date >}} Module 3 {{< /date >}}
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@@ -1,29 +0,0 @@
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---
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title: "Memory Usage Implications"
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description: Understand the memory challenges of multi-vector search and overview of optimization techniques.
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weight: 1
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---
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{{< date >}} Module 3 {{< /date >}}
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# Memory Usage Implications
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Multi-vector search can consume 10-100x more memory than single-vector search. Before deploying to production, you need to understand why this happens and what you can do about it.
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This lesson sets the stage for the optimization techniques we'll explore in the rest of Module 3.
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---
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<div class="video">
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<iframe
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src="https://www.youtube.com/embed/dQw4w9WgXcQ"
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frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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referrerpolicy="strict-origin-when-cross-origin"
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allowfullscreen>
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</iframe>
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</div>
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---
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Let's start optimizing. First up: vector quantization techniques to reduce memory usage.
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@@ -1,7 +1,7 @@
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---
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title: "Multi-Stage Retrieval with Universal Query API"
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description: Combine multiple optimization techniques in multi-stage retrieval pipelines using Qdrant's Universal Query API.
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weight: 5
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weight: 1
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---
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{{< date >}} Module 3 {{< /date >}}
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