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docs/contents/core/efficient_ai/efficient_ai.html

@@ -801,10 +801,10 @@ <h2 data-number="9.2" class="anchored" data-anchor-id="ai-efficiency-evolution">
 <li><p><strong>Compute Efficiency:</strong> Compute efficiency addresses the effective utilization of computational resources, including energy and hardware infrastructure.</p></li>
 <li><p><strong>Data Efficiency:</strong> Data efficiency emphasizes optimizing the amount and quality of data required to achieve desired performance.</p></li>
 </ul>
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 <h3 data-number="9.3.2" class="anchored" data-anchor-id="interdependencies-between-efficiency-dimensions"><span class="header-section-number">9.3.2</span> Interdependencies Between Efficiency Dimensions</h3>
 <p>The efficiency of machine learning systems is inherently a multifaceted challenge that encompasses model design, computational resources, and data utilization. These dimensions—algorithmic efficiency, compute efficiency, and data efficiency—are deeply interdependent, forming a dynamic ecosystem where improvements in one area often ripple across the others. Understanding these interdependencies is crucial for building scalable, cost-effective, and high-performing systems that can adapt to diverse application demands.</p>
 <p>This interplay is best captured through a conceptual visualization. <a href="#fig-interdependece" class="quarto-xref">Figure&nbsp;<span>9.4</span></a> illustrates how these efficiency dimensions overlap and interact with each other in a simple Venn diagram. Each circle represents one of the efficiency dimensions, and their intersections highlight the areas where they influence one another, which we will explore next.</p>
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 <h4 class="anchored" data-anchor-id="the-virtuous-cycle-of-machine-learning-systems">The Virtuous Cycle of Machine Learning Systems</h4>
 <p>Efficiency, scalability, and sustainability are deeply interconnected, forming a virtuous cycle that propels machine learning systems toward broader impact. Efficient systems enable scalable deployments, which amplify their sustainability benefits. In turn, sustainable practices drive the need for more efficient designs, ensuring the cycle continues. This interplay creates systems that are not only technically impressive but also socially and environmentally responsible, aligning AI innovation with the needs of a global community.</p>
 <p><a href="#fig-virtuous-cycle" class="quarto-xref">Figure&nbsp;<span>9.5</span></a> below illustrates the virtuous cycle of machine learning systems. It highlights how efficiency drives scalability, scalability fosters sustainability, and sustainability reinforces efficiency.</p>
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