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      <image:caption>Your Data Captains at work (or slacking?) at their last retreat.</image:caption>
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      <image:caption>Linear regression of a house unit price (10,000 New Taiwan Dollars/Ping) on the logarithm of the distance (in meters) of the house from the closest Mass Rapid Transit station.</image:caption>
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      <image:title>Blog - Building and Load-Testing a Machine Learning Service - Make it stand out</image:title>
      <image:caption>Top chart: requests per second and failed requests per second (single machine). Bottom chart: number of users.</image:caption>
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      <image:caption>Machine Learning service schema.</image:caption>
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      <image:title>Blog - Building and Load-Testing a Machine Learning Service - Make it stand out</image:title>
      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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      <image:title>Blog - Building and Load-Testing a Machine Learning Service - Make it stand out</image:title>
      <image:caption>Top chart: requests per second and failed requests per second (with auto-scaling). Bottom chart: number of users.</image:caption>
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      <image:title>Blog - Building and Load-Testing a Machine Learning Service - Make it stand out</image:title>
      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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      <image:title>Blog - 3 Tactics to Improve your Cluster Analysis - Make it stand out</image:title>
      <image:caption>Cross-tabulation of clusters found using k-means with Silhouette score tuning and Agglomerative Clustering with Davies-Boulding score tuning.</image:caption>
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      <image:title>Blog - 3 Tactics to Improve your Cluster Analysis - Make it stand out</image:title>
      <image:caption>k-means clustering of the mall customers dataset (k=6).</image:caption>
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      <image:title>Blog - 3 Tactics to Improve your Cluster Analysis - Make it stand out</image:title>
      <image:caption>Davies-Bouldin score for different k-means clusterings of the mall customers dataset (lower values of the score are better).</image:caption>
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      <image:title>Blog - 3 Tactics to Improve your Cluster Analysis - Make it stand out</image:title>
      <image:caption>Silhouette score for different Agglomerative Clustering clusterings of the mall customers dataset.</image:caption>
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      <image:title>Blog - 3 Tactics to Improve your Cluster Analysis - Make it stand out</image:title>
      <image:caption>k-means clustering of the mall customers dataset (k=5).</image:caption>
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      <image:title>Blog - 3 Tactics to Improve your Cluster Analysis - Make it stand out</image:title>
      <image:caption>Calinski-Harabasz score for different k-means clusterings of the mall customers dataset.</image:caption>
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      <image:title>Blog - 3 Tactics to Improve your Cluster Analysis - Make it stand out</image:title>
      <image:caption>Cross-tabulation of clusters found using k-means with Silhouette score tuning and Agglomerative Clustering with Silhouette score tuning.</image:caption>
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      <image:title>Blog - 3 Tactics to Improve your Cluster Analysis - Make it stand out</image:title>
      <image:caption>Davies-Bouldin score for different Agglomerative Clustering clusterings of the mall customers dataset (lower values of the score are better).</image:caption>
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      <image:title>Blog - 3 Tactics to Improve your Cluster Analysis - Make it stand out</image:title>
      <image:caption>Silhouette score for different k-means clusterings of the mall customers dataset.</image:caption>
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      <image:title>Blog - 3 Tactics to Improve your Cluster Analysis - Make it stand out</image:title>
      <image:caption>Calinski-Harabasz score for different Agglomerative Clustering clusterings of the mall customers dataset.</image:caption>
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      <image:title>Blog - 3 Tactics to Improve your Cluster Analysis - Make it stand out</image:title>
      <image:caption>Pair plot of the mall customers dataset.</image:caption>
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      <image:title>Blog - 3 Tactics to Improve your Cluster Analysis - Make it stand out</image:title>
      <image:caption>Clusters in the mall customers dataset.</image:caption>
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      <image:title>Blog - 3 Tactics to Improve your Cluster Analysis - Make it stand out</image:title>
      <image:caption>Calinski-Harabasz score for different k-means clusterings of the mall customers dataset. The score is maximized by a trivial clustering structure where each customer is a separate singleton cluster (k=200). However, the (local) maximum of interest is at k=6.</image:caption>
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      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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      <image:caption>Definitions from the Amazon SageMaker SLA for the Online Inference Function.</image:caption>
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      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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      <image:title>Blog - A Guide to Data Roles - Make it stand out</image:title>
      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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