{"id":9256,"date":"2023-09-24T04:04:21","date_gmt":"2023-09-24T02:04:21","guid":{"rendered":"https:\/\/myoceane.fr\/?p=9256"},"modified":"2024-01-01T17:34:45","modified_gmt":"2024-01-01T16:34:45","slug":"%e5%be%ae%e8%bb%9f%e8%aa%8d%e8%ad%89-microsoft-certification-dp-100","status":"publish","type":"post","link":"https:\/\/myoceane.fr\/index.php\/%e5%be%ae%e8%bb%9f%e8%aa%8d%e8%ad%89-microsoft-certification-dp-100\/","title":{"rendered":"[\u5fae\u8edf\u8a8d\u8b49] Microsoft Certification DP-100"},"content":{"rendered":"<div id=\"fb-root\"><\/div>\n<p style=\"text-align: justify;\">\u6ab8\u6aac\u7238\u5728\u8003\u5b8c <a href=\"https:\/\/myoceane.fr\/index.php\/azure-microsoft-certification\/\">Microsoft AZ-104<\/a> \u4e4b\u5f8c\uff0c\u53c8\u6709\u65b0\u7684\u9700\u6c42\u91dd\u5c0d\u7684\u662f <a href=\"https:\/\/learn.microsoft.com\/en-us\/certifications\/exams\/dp-100\/\">Microsoft Certification DP-100<\/a>\uff0c\u9019\u500b\u8a8d\u8b49\u662f\u70ba\u60f3\u8981\u6210\u70ba Azure Data Scientist \u7684\u4eba\u8a2d\u8a08\u7684\uff0c\u800c\u6ab8\u6aac\u7238\u7684\u5922\u60f3\u4e4b\u4e00\u5c31\u662f\u6210\u70ba\u4e00\u540d\u8cc7\u6599\u79d1\u5b78\u5bb6\uff0c\u6240\u4ee5\u4e8c\u8a71\u4e0d\u8aaa\u5c31\u958b\u59cb\u5b78\u7fd2\u9019\u4e00\u500b\u8a8d\u8b49\u7684\u5167\u5bb9\uff0c\u8207 AZ-104 \u8003\u8a66\u76f8\u540c\uff0c<a href=\"https:\/\/learn.microsoft.com\/en-us\/certifications\/azure-data-scientist\/\">\u5fae\u8edf\u6709\u63d0\u4f9b\u514d\u8cbb\u7684\u6559\u6750<\/a>\uff0c\u5982\u679c\u6709\u9700\u8981\u9032\u4e00\u6b65\u7684\u6559\u5b78\u4e5f\u6709\u76f8\u5c0d\u61c9\u7684\u8ab2\u7a0b\u53ef\u4ee5\u8cfc\u8cb7\uff0c\u6559\u6750\u53ea\u80fd\u7b97\u662f\u5c0e\u8b80\uff0c\u771f\u6b63\u9700\u8981\u7684\u5be6\u4f5c\u7684\u7d93\u9a57\uff0c\u5982\u679c\u6c92\u6709\u5be6\u4f5c\u7684\u7d93\u9a57\uff0c\u6ab8\u6aac\u7238\u4e5f\u5f88\u63a8\u85a6\u76f4\u63a5\u53bb\u8b80 Microsoft Azure \u7684 documentation \u7db2\u7ad9\uff0c\u88e1\u9762\u63d0\u4f9b\u4e86\u66f4\u8a73\u7d30\u7684\u89e3\u91cb\u3002<\/p>\n<h5>\u8ab2\u7a0b\u5167\u5bb9\uff1a<\/h5>\n<ul>\n<li>Explore the Azure Machine Learning Workspace<\/li>\n<li>Work with data in Azure Machine Learning<\/li>\n<li>Automate machine learning model selection with Azure Machine Learning<\/li>\n<li>Train models with scripts in Azure Machine Learning<\/li>\n<li>Optimize model training with pipelines in Azure Machine Learning<\/li>\n<li>Deploy and consume models with Azure Machine Learning<\/li>\n<\/ul>\n<h5>Azure \u5be6\u4f5c\u7df4\u7fd2\uff1a<\/h5>\n\n\n<blockquote class=\"wp-block-quote has-text-align-center is-layout-flow wp-block-quote-is-layout-flow\">\n<p>Microsoft Learn &#8211; Hands On Exercises<\/p>\n<cite><a href=\"https:\/\/microsoftlearning.github.io\/mslearn-azure-ml\/\">https:\/\/microsoftlearning.github.io\/mslearn-azure-ml\/<\/a><\/cite><\/blockquote>\n\n\n<p>\u9019\u500b Tutorials \u5305\u542b\uff1a<\/p>\n<ul>\n<li><a href=\"https:\/\/microsoftlearning.github.io\/mslearn-azure-ml\/Instructions\/02-Explore-Azure-Machine-Learning.html\">Explore the Azure Machine Learning workspace<\/a><\/li>\n<li><a href=\"https:\/\/microsoftlearning.github.io\/mslearn-azure-ml\/Instructions\/02-Explore-developer-tools.html\">Explore developer tools for workspace interaction<\/a><\/li>\n<li><a href=\"https:\/\/microsoftlearning.github.io\/mslearn-azure-ml\/Instructions\/03-Make-data-available.html\">Make data available in Azure Machine Learning<\/a><\/li>\n<li><a href=\"https:\/\/microsoftlearning.github.io\/mslearn-azure-ml\/Instructions\/04-Work-with-compute.html\">Work with compute resources in Azure Machine Learning<\/a><\/li>\n<li><a href=\"https:\/\/microsoftlearning.github.io\/mslearn-azure-ml\/Instructions\/04-Work-with-environments.html\">Work with environments in Azure Machine Learning<\/a><\/li>\n<li><a href=\"https:\/\/microsoftlearning.github.io\/mslearn-azure-ml\/Instructions\/05-Designer-train-model.html\">Train a model with the Azure Machine Learning Designer<\/a><\/li>\n<li><a href=\"https:\/\/microsoftlearning.github.io\/mslearn-azure-ml\/Instructions\/06-AutoML-classification-model.html\">Find the best classification model with Automated Machine Learning<\/a><\/li>\n<li><a href=\"https:\/\/microsoftlearning.github.io\/mslearn-azure-ml\/Instructions\/07-Notebooks-mlflow-tracking.html\">Track model training in notebooks with MLflow<\/a><\/li>\n<li>Run a training script as a command job in Azure Machine Learning<\/li>\n<li>Use MLflow to track training jobs<\/li>\n<li>Performs hyperparameter tuning with a sweep job<\/li>\n<li>Run pipelines in Azure Machine Learning<\/li>\n<li>Create and explore the Responsible AI dashboard<\/li>\n<li>Log and register models with MLflow<\/li>\n<li>Deploy a model to a batch endpoint<\/li>\n<li>Deploy a model to a managed online endpoint<\/li>\n<\/ul>\n<h5><a href=\"https:\/\/www.vcesoft.com\/demo\/DP-100.pdf\">\u514d\u8cbb 30 \u984c\u8003\u53e4\u984c<\/a><\/h5>\n<p>\u5f88\u63a8\u85a6\u5148\u505a\u5b8c\u9019\u4e09\u5341\u984c\u8003\u53e4\u984c\u6709\u4e00\u500b\u611f\u89ba\u77e5\u9053\u8003\u8a66\u5927\u6982\u6703\u51fa\u4ec0\u9ebc\u6a23\u7684\u984c\u76ee\u3002<\/p>\n<h5>\u514d\u8cbb\u7684\u8003\u53e4\u984c\u8b1b\u89e3\u9023\u7d50<\/h5>\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"DP-100 Practice Test (Design &amp; Implement a Data Science Solution on Azure) [for Data Scientist]\" width=\"910\" height=\"512\" src=\"https:\/\/www.youtube.com\/embed\/Mv0dxFzdzbA?start=395&#038;feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n<p style=\"text-align: justify;\">\u5982\u679c\u559c\u6b61\u4ee5\u4e0a\u7684 15 \u984c Practice Test \u9032\u4e00\u6b65\u53ef\u4ee5\u8cfc\u8cb7\uff1a<a href=\"https:\/\/www.whizlabs.com\/microsoft-azure-certification-dp-100\/\">https:\/\/www.whizlabs.com\/microsoft-azure-certification-dp-100\/<\/a><\/p>\n<h5>\u5176\u4ed6\u91cd\u8981\u7684 Azure Machine Learning Documentation Site<\/h5>\n<ul>\n<li><a href=\"https:\/\/learn.microsoft.com\/en-us\/previous-versions\/azure\/machine-learning\/studio-module-reference\/smote\">SMOTE (Synthetic Minority Oversampling Technique) \u6982\u5ff5\u89e3\u8aaa<\/a><\/li>\n<li><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/concept-automated-ml?view=azureml-api-2\">AutoML (Automated Machine Learning) \u5be6\u4f5c<\/a><\/li>\n<li><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/concept-endpoints?view=azureml-api-2\">Endpoints for Inferencing<\/a><\/li>\n<\/ul>\n<h4>\u66f4\u591a Microsoft Developer \u5f71\u7247\u8b1b\u89e3 Azure Machine Learning\u00a0<\/h4>\n<ul>\n<li class=\"style-scope ytd-watch-metadata\"><a href=\"https:\/\/www.youtube.com\/watch?v=WZ7vS10KPAw&amp;t=241s\">Model deployment and inferencing with Azure Machine Learning<\/a><\/li>\n<\/ul>\n<h4>Basic v.s. Enterprise Machine Learning \u5dee\u5225\u662f\u4ec0\u9ebc\uff1f<a href=\"https:\/\/learn.microsoft.com\/en-us\/shows\/AI-Show\/New-Azure-Machine-Learning-Basic\">\u53c3\u8003\u9023\u7d50<\/a><\/h4>\n<p>\u6700\u4e3b\u8981\u7684\u5dee\u7570\u5c31\u662f\u5728\u662f\u5426\u6709 UI \u4ecb\u9762\u53ef\u4ee5 no-code \u5be6\u4f5c ML\u3002<\/p>\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Basic<\/strong> <\/td><td><strong>Enterprise<\/strong><\/td><\/tr><tr><td><strong>One stop destination for open source<br><\/strong>development (support for MLflow, ONNX for TensorRT\/Intel nGraph, PyTorch, hosted notebooks)<\/td><td><strong>Enterprise-ready <br><\/strong>security, manageability, lineage, collaboration and enhanced goverance for large scale ML workloads.<\/td><\/tr><tr><td>Best <strong>code-first experience<\/strong> using existing feature\/tools at cloud scale<\/td><td><strong>ML solutions for all<\/strong>: Drag and drop UI and no-code automated ML UI.<\/td><\/tr><tr><td><strong>DIY machine learning <\/strong>lifecycle management\/MLOps<\/td><td><strong>Comprehensive machine learning lifecycle<\/strong> managemenent plus MLOps UI<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\">Basic v.s. Enterprise Machine Learning<\/figcaption><\/figure>\n\n\n<h4>Categories of Parity \u512a\u5316\u7684\u76ee\u6a19\u5747\u52e2<\/h4>\n<p>\u5728\u8a13\u7df4\u6a21\u578b\u7684\u6642\u5019\u91dd\u5c0d\u4e0d\u540c\u985e\u578b\u7684\u61c9\u7528 (e.g. Classification or Regression) \u53ef\u4ee5\u5c0e\u5165\u4e0d\u540c\u7684 Parity \u78ba\u4fdd\u8a13\u7df4\u51fa\u4f86\u7684\u6a21\u578b\u4e0d\u6703\u904e\u5ea6\u504f\u9817\u67d0\u4e00\u985e\u578b\u7684\u8cc7\u6599\u3002<\/p>\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Demography Parity<\/strong><\/td><td>This constraint tries to ensure that an equal number of positive predictions are made in each group.<\/td><\/tr><tr><td><strong>True Positive Rate Parity<\/strong><\/td><td>This constraint tries to ensure that each group contains a comparable ratio of true positive predictions.<\/td><\/tr><tr><td><strong>False Positive Rate Parity<\/strong><\/td><td>This constraint tries to ensure that each group contains a comparable ratio of flase positive predictions.<\/td><\/tr><tr><td><strong>Equalized Odds<\/strong><\/td><td>This constraint tries to ensure that each group contains a comparable ratio of true positive and false positive predictions.<\/td><\/tr><tr><td><strong>Error Rate Parity<\/strong><\/td><td>This constraint used with any of the reduction-based mitigation algorithms (Expontentiated Gradient and Grid Search) to ensure that the error for each sensitive feature group does not deviate from the overall error rate by more that a specified amount.<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\"><strong>Parity used in classification models<\/strong> <\/figcaption><\/figure>\n\n\n<p>\u53e6\u5916\u91dd\u5c0d Regression \u7684\u6709\uff1a<\/p>\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Bounded Group Loss<\/strong><\/td><td>Use this constraint with any of the reduction-based mitigation algorithms to restrict the loss for each sensitive feature group in a regression model.<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\"><strong>Parity used in regression models<\/strong><\/figcaption><\/figure>\n\n\n<h4><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/concept-compute-target?view=azureml-api-2\"><strong>Types of Compute Targets<\/strong><\/a><\/h4>\n<p>\u91dd\u5c0d\u4e0d\u540c\u9700\u6c42\uff0cAzure Machine Learning Studio \u53ef\u4ee5\u652f\u63f4\u4e0d\u540c\u7a2e\u985e\u7684\u904b\u7b97\u8cc7\u6e90\uff0c\u4ee5\u4e0b\u6574\u7406\u5e7e\u500b\u5e38\u7528\u7684\u904b\u7b97\u7e3d\u985e\u8207\u5b83\u7684\u4f7f\u7528\u5834\u666f\uff1a<\/p>\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td class=\"has-text-align-center\" data-align=\"center\"><\/td><td class=\"has-text-align-center\" data-align=\"center\">Automated Machine Learning (AutoML)<\/td><td class=\"has-text-align-center\" data-align=\"center\">Machine Learning Pipelines<\/td><td class=\"has-text-align-center\" data-align=\"center\">Azure Machine Learning Designer<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\"><strong>Local Instance<\/strong><\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes<\/td><td class=\"has-text-align-center\" data-align=\"center\"><\/td><td class=\"has-text-align-center\" data-align=\"center\"><\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\"><span><a style=\"font-weight: bold;\" href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/concept-compute-instance?view=azureml-api-2\">AML Compute Instance<\/a><\/span><\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes<\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes<\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\"><strong><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/how-to-use-serverless-compute?view=azureml-api-2\">AML Serverless Compute<\/a><\/strong><\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes<\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes<\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\"><strong><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/how-to-create-compute-instance?view=azureml-api-2\">AML Compute Cluster<\/a><\/strong><\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes (SDK)<\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes<\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\"><strong><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/how-to-attach-kubernetes-anywhere?view=azureml-api-2\">AML Kubernetes<\/a><\/strong><\/td><td class=\"has-text-align-center\" data-align=\"center\"><\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes<\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\"><strong>Remote VM<\/strong><\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes<\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes<\/td><td class=\"has-text-align-center\" data-align=\"center\"><\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\"><strong>Apache Spark Pools<\/strong><\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes (SDK local mode only)<\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes<\/td><td class=\"has-text-align-center\" data-align=\"center\"><\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\"><strong>Azure Databricks<\/strong><\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes (SDK local mode only)<\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes<\/td><td class=\"has-text-align-center\" data-align=\"center\"><\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\"><strong>Azure Data Lake Analytics<\/strong><\/td><td class=\"has-text-align-center\" data-align=\"center\"><\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes<\/td><td class=\"has-text-align-center\" data-align=\"center\"><\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\"><strong>Azure HDInsight<\/strong><\/td><td class=\"has-text-align-center\" data-align=\"center\"><\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes<\/td><td class=\"has-text-align-center\" data-align=\"center\"><\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\"><strong>Azure Batch<\/strong><\/td><td class=\"has-text-align-center\" data-align=\"center\"><\/td><td class=\"has-text-align-center\" data-align=\"center\">Yes<\/td><td class=\"has-text-align-center\" data-align=\"center\"><\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\">Compute Targets \u4f7f\u7528\u5834\u666f<\/figcaption><\/figure>\n\n\n<p style=\"text-align: justify;\">Notes:<\/p>\n<ol>\n<li style=\"text-align: justify;\"><strong>Local Instances:<\/strong> It may be your physical workstation. Local compute is generally a great choice during development and testing with low to moderate volumes of data.<\/li>\n<li style=\"text-align: justify;\"><strong>Compute Clusters:<\/strong> For experiment workloads with high scalability requirements, you can use Azure Machine Learning compute clusters; which are multi-node clusters of Virtual Machines that automatically scale up or down to meet demand. This is a cost-effective way to run experiments that need to handle large volumes of data or use parallel processing to distribute the workload and reduce the time it takes to run.<\/li>\n<li style=\"text-align: justify;\"><strong>Attached Compute (Azure Databricks, Data Lake Analytics, HDInsight, Synapse Spark pool, Virtual machine):<\/strong> If you already use an Azure-based compute environment for data science, such as a virtual machine or an Azure Databricks cluster, you can attach it to your Azure Machine Learning workspace and use it as a compute target for certain types of workload.<\/li>\n<\/ol>\n<h4><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/concept-ml-pipelines?view=azureml-api-2#standardize-the-mlops-practice-and-support-scalable-team-collaboration\">What are Azure Machine Learning Pipelines ?<\/a><\/h4>\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td>Scenario<\/td><td>Primary persona<\/td><td>Azure offering<\/td><td>OSS offering<\/td><td>Canonical pipe<\/td><td>Strengths<\/td><\/tr><tr><td>Model Orchestration (Machine learning)<\/td><td>Data Scientist<\/td><td>Azure Machine Learning Pipelines<\/td><td>Kubeflow Pipelines<\/td><td>Data -&gt; Model<\/td><td>Distribution, caching, code-first, reuse<\/td><\/tr><tr><td>Data Orchestration (Data prep)<\/td><td>Data Engineer<\/td><td>Azure Data Factory pipelines<\/td><td>Apache Airflow<\/td><td>Data -&gt; Data<\/td><td>Strongly typed movement, data-centric activities<\/td><\/tr><tr><td>Code &amp; App Orchestration (CI\/CD)<\/td><td>App Developer \/ Ops<\/td><td>Azure Pipelines<\/td><td>Jenkins<\/td><td>Code + Model -&gt; App\/Service<\/td><td>Most open and flexible activity support, approval queues, phases with gating<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\">Azure cloud provides several types of pipeline<\/figcaption><\/figure>\n\n\n<h4><strong>Types of Datasets \u4e3b\u8981\u5206\u6210\u00a0<\/strong><\/h4>\n<ul>\n<li>TabularDataset<\/li>\n<li>FileDataset<\/li>\n<li>FolderDataset<\/li>\n<\/ul>\n<h4><strong>Types of Datastores \u5247\u662f\u5132\u5b58 Datasets \u7684\u5730\u65b9\u4e3b\u8981\u5206\u6210<\/strong><\/h4>\n<ul>\n<li>Azure Blob Container<\/li>\n<li>Azure File Share<\/li>\n<li>Azure Data Lake<\/li>\n<li>Azure Data Lake Gen2<\/li>\n<li>Azure SQL Database<\/li>\n<li>Azure Database for PostgreSQL<\/li>\n<li>Databricks File System<\/li>\n<li>Azure Database for MySQL<\/li>\n<\/ul>\n<h4><a href=\"https:\/\/www.aiproblog.com\/index.php\/2020\/06\/11\/ordinal-and-one-hot-encodings-for-categorical-data\/\">Ordinal Encoding v.s. One hot Encoding v.s. Dummy Variable Encoding<\/a><\/h4>\n<h5><a href=\"https:\/\/learn.microsoft.com\/en-us\/previous-versions\/azure\/machine-learning\/studio-module-reference\/evaluate-model\">Performance Metrics:<\/a><\/h5>\n<ul>\n<li>Regression Model<\/li>\n<\/ul>\n\n\n\n<p class=\"ql-center-displayed-equation\" style=\"line-height: 239px;\"><span class=\"ql-right-eqno\"> (1) <\/span><span class=\"ql-left-eqno\"> &nbsp; <\/span><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/myoceane.fr\/wp-content\/ql-cache\/quicklatex.com-5f5fca5a77373dda21020bc55d58e97c_l3.png\" height=\"239\" width=\"536\" class=\"ql-img-displayed-equation quicklatex-auto-format\" alt=\"&#92;&#98;&#101;&#103;&#105;&#110;&#123;&#97;&#108;&#105;&#103;&#110;&#42;&#125; &#92;&#116;&#101;&#120;&#116;&#114;&#109;&#123;&#77;&#101;&#97;&#110;&#32;&#65;&#98;&#115;&#111;&#108;&#117;&#116;&#101;&#32;&#69;&#114;&#114;&#111;&#114;&#58;&#32;&#125;&#77;&#65;&#69;&#32;&#38;&#61;&#32;&#92;&#100;&#102;&#114;&#97;&#99;&#123;&#49;&#125;&#123;&#78;&#125;&#92;&#109;&#97;&#116;&#104;&#108;&#97;&#114;&#103;&#101;&#114;&#123;&#92;&#115;&#117;&#109;&#125;&#95;&#123;&#105;&#61;&#49;&#125;&#94;&#123;&#78;&#125;&#124;&#121;&#95;&#105;&#32;&#45;&#32;&#92;&#104;&#97;&#116;&#123;&#121;&#125;&#124;&#32;&#92;&#92; &#92;&#116;&#101;&#120;&#116;&#114;&#109;&#123;&#77;&#101;&#97;&#110;&#32;&#83;&#113;&#117;&#97;&#114;&#101;&#100;&#32;&#69;&#114;&#114;&#111;&#114;&#58;&#32;&#125;&#77;&#83;&#69;&#32;&#38;&#61;&#32;&#92;&#100;&#102;&#114;&#97;&#99;&#123;&#49;&#125;&#123;&#78;&#125;&#92;&#109;&#97;&#116;&#104;&#108;&#97;&#114;&#103;&#101;&#114;&#123;&#92;&#115;&#117;&#109;&#125;&#95;&#123;&#105;&#61;&#49;&#125;&#94;&#123;&#78;&#125;&#40;&#121;&#95;&#105;&#45;&#92;&#104;&#97;&#116;&#123;&#121;&#125;&#41;&#94;&#50;&#32;&#92;&#92; &#92;&#116;&#101;&#120;&#116;&#114;&#109;&#123;&#82;&#111;&#111;&#116;&#32;&#77;&#101;&#97;&#110;&#32;&#83;&#113;&#117;&#97;&#114;&#101;&#100;&#32;&#69;&#114;&#114;&#111;&#114;&#58;&#32;&#125;&#82;&#77;&#83;&#69;&#32;&#38;&#61;&#32;&#92;&#115;&#113;&#114;&#116;&#123;&#77;&#83;&#69;&#125;&#32;&#61;&#32;&#92;&#115;&#113;&#114;&#116;&#123;&#92;&#100;&#102;&#114;&#97;&#99;&#123;&#49;&#125;&#123;&#78;&#125;&#92;&#109;&#97;&#116;&#104;&#108;&#97;&#114;&#103;&#101;&#114;&#123;&#92;&#115;&#117;&#109;&#125;&#95;&#123;&#105;&#61;&#49;&#125;&#94;&#123;&#78;&#125;&#40;&#121;&#95;&#105;&#45;&#92;&#104;&#97;&#116;&#123;&#121;&#125;&#41;&#94;&#50;&#125;&#32;&#92;&#92; &#92;&#116;&#101;&#120;&#116;&#114;&#109;&#123;&#82;&#101;&#108;&#97;&#116;&#105;&#118;&#101;&#32;&#83;&#113;&#117;&#97;&#114;&#101;&#32;&#69;&#114;&#114;&#111;&#114;&#58;&#32;&#125;&#82;&#83;&#69;&#32;&#38;&#61;&#32;&#92;&#102;&#114;&#97;&#99;&#123;&#92;&#115;&#117;&#109;&#95;&#105;&#94;&#78;&#32;&#40;&#121;&#95;&#105;&#45;&#92;&#104;&#97;&#116;&#123;&#121;&#125;&#95;&#105;&#41;&#94;&#50;&#125;&#123;&#92;&#115;&#117;&#109;&#95;&#105;&#94;&#78;&#32;&#40;&#121;&#95;&#105;&#45;&#92;&#98;&#97;&#114;&#123;&#121;&#125;&#41;&#94;&#50;&#125;&#32;&#92;&#92; &#92;&#116;&#101;&#120;&#116;&#114;&#109;&#123;&#67;&#111;&#101;&#102;&#102;&#105;&#99;&#105;&#101;&#110;&#116;&#32;&#111;&#102;&#32;&#100;&#101;&#116;&#101;&#114;&#109;&#105;&#110;&#97;&#116;&#105;&#111;&#110;&#58;&#32;&#125;&#82;&#94;&#50;&#32;&#38;&#61;&#32;&#49;&#45;&#82;&#83;&#69;&#32;&#61;&#32;&#49;&#32;&#45;&#32;&#92;&#102;&#114;&#97;&#99;&#123;&#92;&#115;&#117;&#109;&#95;&#105;&#94;&#78;&#32;&#40;&#121;&#95;&#105;&#45;&#92;&#104;&#97;&#116;&#123;&#121;&#125;&#95;&#105;&#41;&#94;&#50;&#125;&#123;&#92;&#115;&#117;&#109;&#95;&#105;&#94;&#78;&#32;&#40;&#121;&#95;&#105;&#45;&#92;&#98;&#97;&#114;&#123;&#121;&#125;&#41;&#94;&#50;&#125;&#44; &#92;&#101;&#110;&#100;&#123;&#97;&#108;&#105;&#103;&#110;&#42;&#125;\" title=\"Rendered by QuickLaTeX.com\"\/><\/p>\nwhere <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/myoceane.fr\/wp-content\/ql-cache\/quicklatex.com-365557f5f12703d4de96202aa97b83e8_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#92;&#104;&#97;&#116;&#123;&#121;&#125;&#95;&#105;\" title=\"Rendered by QuickLaTeX.com\" height=\"16\" width=\"14\" style=\"vertical-align: -4px;\"\/> is the predicted value of <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/myoceane.fr\/wp-content\/ql-cache\/quicklatex.com-bb3c186e5c65fcd066bb23dec8f4e48a_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#121;&#95;&#105;\" title=\"Rendered by QuickLaTeX.com\" height=\"12\" width=\"14\" style=\"vertical-align: -4px;\"\/> and <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/myoceane.fr\/wp-content\/ql-cache\/quicklatex.com-1779f1f54d2b68e1709fbb5383e130ea_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#92;&#98;&#97;&#114;&#123;&#121;&#125;&#32;&#61;&#32;&#69;&#91;&#121;&#95;&#105;&#93;\" title=\"Rendered by QuickLaTeX.com\" height=\"18\" width=\"69\" style=\"vertical-align: -5px;\"\/>\n\n\n<ul>\n<li>Classification Model\n<ul>\n<li style=\"text-align: justify;\"><em><strong>AUC<\/strong><\/em> measures the area under the curve plotted with true positives on the y axis and false positives on the x axis.<\/li>\n<li style=\"text-align: justify;\"><em><strong>Average log loss<\/strong><\/em> is a single score used to express the penalty for wrong results. It is calculated as the difference between two probability distributions \u2013 the true one, and the one in the model.<\/li>\n<li style=\"text-align: justify;\"><em><strong>Training log loss<\/strong><\/em> is a single score that represents the advantage of the classifier over a random prediction. The log loss measures the uncertainty of your model by comparing the probabilities it outputs to the known values (ground truth) in the labels. You want to minimize log loss for the model as a whole.<\/li>\n<li style=\"text-align: justify;\"><strong><em>Other metrics<\/em><\/strong> defined by\u00a0four following numbers: <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/myoceane.fr\/wp-content\/ql-cache\/quicklatex.com-0e7fe4ff12d64a0eda98bdff8af4564b_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#84;&#80;\" title=\"Rendered by QuickLaTeX.com\" height=\"12\" width=\"27\" style=\"vertical-align: 0px;\"\/>, <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/myoceane.fr\/wp-content\/ql-cache\/quicklatex.com-5246c6f8189a89de57d7af3d0043805e_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#84;&#78;\" title=\"Rendered by QuickLaTeX.com\" height=\"12\" width=\"29\" style=\"vertical-align: 0px;\"\/>, <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/myoceane.fr\/wp-content\/ql-cache\/quicklatex.com-9f9033e93b64b1685de6c639e740fd53_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#70;&#80;\" title=\"Rendered by QuickLaTeX.com\" height=\"12\" width=\"28\" style=\"vertical-align: 0px;\"\/>, <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/myoceane.fr\/wp-content\/ql-cache\/quicklatex.com-1d83e17e342e775caa00edc33e0c10e6_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#70;&#78;\" title=\"Rendered by QuickLaTeX.com\" height=\"12\" width=\"30\" style=\"vertical-align: 0px;\"\/>, which represents number of samples in True Positive, True Negative, False Positive, False Negative four cases respectively.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p class=\"ql-center-displayed-equation\" style=\"line-height: 173px;\"><span class=\"ql-right-eqno\"> (2) <\/span><span class=\"ql-left-eqno\"> &nbsp; <\/span><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/myoceane.fr\/wp-content\/ql-cache\/quicklatex.com-8a3c93bad3dc600400adb7576de852b0_l3.png\" height=\"173\" width=\"376\" class=\"ql-img-displayed-equation quicklatex-auto-format\" alt=\"&#92;&#98;&#101;&#103;&#105;&#110;&#123;&#97;&#108;&#105;&#103;&#110;&#42;&#125; &#92;&#116;&#101;&#120;&#116;&#114;&#109;&#123;&#65;&#99;&#99;&#117;&#114;&#97;&#99;&#121;&#125;&#32;&#38;&#61;&#32;&#92;&#102;&#114;&#97;&#99;&#123;&#84;&#80;&#32;&#43;&#32;&#84;&#78;&#125;&#123;&#84;&#80;&#32;&#43;&#32;&#84;&#78;&#32;&#43;&#32;&#70;&#80;&#32;&#43;&#32;&#70;&#78;&#125;&#32;&#92;&#92; &#92;&#116;&#101;&#120;&#116;&#114;&#109;&#123;&#80;&#114;&#101;&#99;&#105;&#115;&#105;&#111;&#110;&#125;&#32;&#38;&#61;&#32;&#92;&#102;&#114;&#97;&#99;&#123;&#84;&#80;&#125;&#123;&#84;&#80;&#32;&#43;&#32;&#70;&#80;&#125;&#32;&#92;&#92;&#32; &#92;&#116;&#101;&#120;&#116;&#114;&#109;&#123;&#82;&#101;&#99;&#97;&#108;&#108;&#44;&#32;&#97;&#107;&#97;&#32;&#83;&#101;&#110;&#115;&#105;&#116;&#105;&#118;&#105;&#116;&#121;&#125;&#32;&#38;&#61;&#32;&#92;&#102;&#114;&#97;&#99;&#123;&#84;&#80;&#125;&#123;&#84;&#80;&#32;&#43;&#32;&#70;&#78;&#125;&#32;&#92;&#92; &#92;&#116;&#101;&#120;&#116;&#114;&#109;&#123;&#70;&#45;&#115;&#99;&#111;&#114;&#101;&#125;&#32;&#38;&#61;&#32;&#50;&#32;&#92;&#116;&#105;&#109;&#101;&#115;&#32;&#92;&#102;&#114;&#97;&#99;&#123;&#112;&#114;&#101;&#99;&#105;&#115;&#105;&#111;&#110;&#32;&#92;&#116;&#105;&#109;&#101;&#115;&#32;&#114;&#101;&#99;&#97;&#108;&#108;&#125;&#123;&#112;&#114;&#101;&#99;&#105;&#115;&#105;&#111;&#110;&#32;&#43;&#32;&#114;&#101;&#99;&#97;&#108;&#108;&#125; &#92;&#101;&#110;&#100;&#123;&#97;&#108;&#105;&#103;&#110;&#42;&#125;\" title=\"Rendered by QuickLaTeX.com\"\/><\/p>\n\n\n<h4>MLflow is composed of 4 elements<\/h4>\n<ul>\n<li><strong>MLflow Models:<\/strong> For example, scikit-learn, Keras, MLlib, ONNX.<\/li>\n<li><strong>MLflow Model Registry:\u00a0<\/strong>It allows data scientists to register models.<\/li>\n<li><strong>MLflow Projects:<\/strong> A way of packaging up code in a manner, which allows for consistent deployment and ability to reproduce results. Several environments including Conda, Docker, etc are supported.<\/li>\n<li><strong>MLflow Tracking:<\/strong> A data scientists may log parameters, versions of libraries used, evaluation metrics, and generated output files when training machine learning models.<\/li>\n<\/ul>\n<h4><strong>Databricks Runtimes<\/strong><\/h4>\n<ul>\n<li style=\"text-align: justify;\"><strong>Databricks Runtime:<\/strong> includes Apache Spark, components and updates that optimize the usability, performance, and security for big data analytics.<\/li>\n<li style=\"text-align: justify;\"><strong>Databricks Runtime for Machine Learning:<\/strong> a variant that adds multiple machine learning libraries such as TensorFlow, Keras and PyTorch.<\/li>\n<li><strong>Databricks Light:<\/strong> for jobs that don&#8217;t need the advanced performance, reliability, or autoscaling of the Databricks Runtime.<\/li>\n<\/ul>\n<h4><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/component-reference\/normalize-data?view=azureml-api-2\">Normalize Data v.s. Standardization<\/a><\/h4>\n\n\n\nThe normalizer scales each value by dividing each value by its magnitude in n-dimensional space for n number of features.\n<p class=\"ql-center-displayed-equation\" style=\"line-height: 50px;\"><span class=\"ql-right-eqno\"> (3) <\/span><span class=\"ql-left-eqno\"> &nbsp; <\/span><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/myoceane.fr\/wp-content\/ql-cache\/quicklatex.com-42e1b5ffcb9ff1854a5165e408b9ec3b_l3.png\" height=\"50\" width=\"436\" class=\"ql-img-displayed-equation quicklatex-auto-format\" alt=\"&#92;&#98;&#101;&#103;&#105;&#110;&#123;&#97;&#108;&#105;&#103;&#110;&#42;&#125; &#40;&#120;&#95;&#123;&#49;&#105;&#125;&#44;&#32;&#120;&#95;&#123;&#50;&#105;&#125;&#44;&#32;&#92;&#100;&#111;&#116;&#115;&#44;&#32;&#120;&#95;&#123;&#110;&#105;&#125;&#41;&#32;&#61;&#32;&#40;&#92;&#102;&#114;&#97;&#99;&#123;&#120;&#95;&#123;&#49;&#105;&#125;&#125;&#123;&#92;&#115;&#113;&#114;&#116;&#123;&#92;&#115;&#117;&#109;&#95;&#106;&#94;&#110;&#32;&#120;&#95;&#123;&#106;&#105;&#125;&#94;&#50;&#125;&#125;&#44;&#32;&#92;&#102;&#114;&#97;&#99;&#123;&#120;&#95;&#123;&#50;&#105;&#125;&#125;&#123;&#92;&#115;&#113;&#114;&#116;&#123;&#92;&#115;&#117;&#109;&#95;&#106;&#94;&#110;&#32;&#120;&#95;&#123;&#106;&#105;&#125;&#94;&#50;&#125;&#125;&#44;&#32;&#92;&#100;&#111;&#116;&#115;&#44;&#32;&#92;&#102;&#114;&#97;&#99;&#123;&#120;&#95;&#123;&#110;&#105;&#125;&#125;&#123;&#92;&#115;&#113;&#114;&#116;&#123;&#92;&#115;&#117;&#109;&#95;&#106;&#94;&#110;&#32;&#120;&#95;&#123;&#106;&#105;&#125;&#94;&#50;&#125;&#125;&#41; &#92;&#101;&#110;&#100;&#123;&#97;&#108;&#105;&#103;&#110;&#42;&#125;\" title=\"Rendered by QuickLaTeX.com\"\/><\/p>\n\n\n\n\n<p class=\"ql-center-displayed-equation\" style=\"line-height: 162px;\"><span class=\"ql-right-eqno\"> (4) <\/span><span class=\"ql-left-eqno\"> &nbsp; <\/span><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/myoceane.fr\/wp-content\/ql-cache\/quicklatex.com-4850fdd615420480be015d96fb9fe7ce_l3.png\" height=\"162\" width=\"277\" class=\"ql-img-displayed-equation quicklatex-auto-format\" alt=\"&#92;&#98;&#101;&#103;&#105;&#110;&#123;&#97;&#108;&#105;&#103;&#110;&#42;&#125; &#92;&#116;&#101;&#120;&#116;&#114;&#109;&#123;&#90;&#115;&#99;&#111;&#114;&#101;&#58;&#32;&#125;&#122;&#95;&#105;&#32;&#38;&#61;&#32;&#92;&#102;&#114;&#97;&#99;&#123;&#120;&#95;&#105;&#32;&#45;&#32;&#109;&#101;&#97;&#110;&#40;&#120;&#41;&#125;&#123;&#115;&#116;&#100;&#101;&#118;&#40;&#120;&#41;&#125;&#32;&#92;&#92; &#92;&#116;&#101;&#120;&#116;&#114;&#109;&#123;&#77;&#105;&#110;&#77;&#97;&#120;&#58;&#32;&#125;&#122;&#95;&#105;&#32;&#38;&#61;&#32;&#92;&#102;&#114;&#97;&#99;&#123;&#120;&#95;&#105;&#32;&#45;&#32;&#109;&#105;&#110;&#40;&#120;&#41;&#125;&#123;&#109;&#97;&#120;&#40;&#120;&#41;&#32;&#45;&#32;&#109;&#105;&#110;&#40;&#120;&#41;&#125;&#32;&#92;&#92; &#92;&#116;&#101;&#120;&#116;&#114;&#109;&#123;&#76;&#111;&#103;&#105;&#115;&#116;&#105;&#99;&#58;&#32;&#125;&#122;&#95;&#105;&#32;&#38;&#61;&#32;&#92;&#102;&#114;&#97;&#99;&#123;&#49;&#125;&#123;&#49;&#32;&#45;&#32;&#101;&#94;&#123;&#45;&#120;&#125;&#125;&#32;&#92;&#92; &#92;&#116;&#101;&#120;&#116;&#114;&#109;&#123;&#76;&#111;&#103;&#110;&#111;&#114;&#109;&#97;&#108;&#58;&#32;&#125;&#122;&#32;&#38;&#61;&#32;&#76;&#111;&#103;&#110;&#111;&#114;&#109;&#97;&#108;&#40;&#120;&#44;&#92;&#109;&#117;&#44;&#92;&#115;&#105;&#103;&#109;&#97;&#41; &#92;&#101;&#110;&#100;&#123;&#97;&#108;&#105;&#103;&#110;&#42;&#125;\" title=\"Rendered by QuickLaTeX.com\"\/><\/p>\n\n\n<h4><a href=\"https:\/\/learn.microsoft.com\/zh-tw\/azure\/synapse-analytics\/machine-learning\/tutorial-horovod-pytorch\">Horovod \u5206\u6563\u5f0f\u795e\u7d93\u7db2\u8def\u8a13\u7df4\u6846\u67b6\u00a0<\/a><\/h4>\n<p style=\"text-align: justify;\">Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.<\/p>\n<h4><a href=\"https:\/\/learn.microsoft.com\/en-us\/previous-versions\/azure\/machine-learning\/studio-module-reference\/filter-based-feature-selection\">Feature Scoring Statistical Method<\/a><\/h4>\n<p style=\"text-align: justify;\">The\u00a0<strong>Filter-Based Feature Selection<\/strong>\u00a0provides a variety of metrics for assessing the information value in each column. This section provides a general description of each metric, and how it is applied.<\/p>\n\n\n\n<p class=\"ql-center-displayed-equation\" style=\"line-height: 208px;\"><span class=\"ql-right-eqno\"> (5) <\/span><span class=\"ql-left-eqno\"> &nbsp; <\/span><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/myoceane.fr\/wp-content\/ql-cache\/quicklatex.com-615b8a6e735d533bd2fa894fe51e30fc_l3.png\" height=\"208\" width=\"548\" class=\"ql-img-displayed-equation quicklatex-auto-format\" alt=\"&#92;&#98;&#101;&#103;&#105;&#110;&#123;&#97;&#108;&#105;&#103;&#110;&#42;&#125; &#92;&#116;&#101;&#120;&#116;&#114;&#109;&#123;&#80;&#101;&#97;&#114;&#115;&#111;&#110;&#32;&#67;&#111;&#114;&#114;&#101;&#108;&#97;&#116;&#105;&#111;&#110;&#32;&#67;&#111;&#101;&#102;&#102;&#105;&#99;&#105;&#101;&#110;&#116;&#58;&#32;&#125;&#32;&#114;&#95;&#123;&#120;&#121;&#125;&#32;&#38;&#61;&#32;&#92;&#102;&#114;&#97;&#99;&#123;&#92;&#115;&#117;&#109;&#95;&#105;&#32;&#40;&#120;&#95;&#105;&#32;&#45;&#32;&#92;&#98;&#97;&#114;&#123;&#120;&#125;&#41;&#40;&#121;&#95;&#105;&#32;&#45;&#32;&#92;&#98;&#97;&#114;&#123;&#121;&#125;&#41;&#125;&#123;&#92;&#115;&#113;&#114;&#116;&#123;&#92;&#115;&#117;&#109;&#95;&#105;&#32;&#40;&#120;&#95;&#105;&#32;&#45;&#32;&#92;&#98;&#97;&#114;&#123;&#120;&#125;&#41;&#94;&#50;&#125;&#92;&#115;&#113;&#114;&#116;&#123;&#92;&#115;&#117;&#109;&#95;&#105;&#32;&#40;&#121;&#95;&#105;&#32;&#45;&#32;&#92;&#98;&#97;&#114;&#123;&#121;&#125;&#41;&#94;&#50;&#125;&#125;&#32;&#92;&#92; &#92;&#116;&#101;&#120;&#116;&#114;&#109;&#123;&#77;&#117;&#116;&#117;&#97;&#108;&#32;&#73;&#110;&#102;&#111;&#114;&#109;&#97;&#116;&#105;&#111;&#110;&#58;&#32;&#125;&#32;&#73;&#95;&#123;&#120;&#121;&#125;&#32;&#38;&#61;&#32;&#92;&#115;&#117;&#109;&#95;&#123;&#121;&#92;&#105;&#110;&#32;&#89;&#125;&#92;&#115;&#117;&#109;&#95;&#123;&#120;&#92;&#105;&#110;&#32;&#88;&#125;&#32;&#112;&#40;&#120;&#44;&#32;&#121;&#41;&#92;&#108;&#111;&#103;&#92;&#102;&#114;&#97;&#99;&#123;&#112;&#40;&#120;&#44;&#121;&#41;&#125;&#123;&#112;&#40;&#120;&#41;&#112;&#40;&#121;&#41;&#125;&#32;&#92;&#92; &#92;&#116;&#101;&#120;&#116;&#114;&#109;&#123;&#83;&#112;&#101;&#97;&#114;&#109;&#97;&#110;&#32;&#67;&#111;&#114;&#114;&#101;&#108;&#97;&#116;&#105;&#111;&#110;&#32;&#67;&#111;&#101;&#102;&#102;&#105;&#99;&#105;&#101;&#110;&#116;&#58;&#32;&#125;&#32;&#114;&#95;&#115;&#32;&#38;&#61;&#32;&#92;&#114;&#104;&#111;&#95;&#123;&#82;&#40;&#88;&#41;&#44;&#82;&#40;&#89;&#41;&#125;&#32;&#61;&#32;&#92;&#102;&#114;&#97;&#99;&#123;&#99;&#111;&#118;&#40;&#82;&#40;&#88;&#41;&#44;&#82;&#40;&#89;&#41;&#41;&#125;&#123;&#92;&#115;&#105;&#103;&#109;&#97;&#95;&#123;&#82;&#40;&#88;&#41;&#125;&#92;&#115;&#105;&#103;&#109;&#97;&#95;&#123;&#82;&#40;&#89;&#41;&#125;&#125;&#32;&#92;&#92; &#92;&#116;&#101;&#120;&#116;&#114;&#109;&#123;&#75;&#101;&#110;&#100;&#97;&#108;&#108;&#32;&#67;&#111;&#114;&#114;&#101;&#108;&#97;&#116;&#105;&#111;&#110;&#32;&#67;&#111;&#101;&#102;&#102;&#105;&#99;&#105;&#101;&#110;&#116;&#58;&#32;&#125;&#32;&#92;&#92; &#92;&#116;&#101;&#120;&#116;&#114;&#109;&#123;&#67;&#104;&#105;&#45;&#83;&#113;&#117;&#97;&#114;&#101;&#100;&#58;&#32;&#125; &#92;&#101;&#110;&#100;&#123;&#97;&#108;&#105;&#103;&#110;&#42;&#125;\" title=\"Rendered by QuickLaTeX.com\"\/><\/p>\n\n\n<p style=\"text-align: justify;\">Note: In Spearman Correlation Coefficient, data X, Y should first pass a ranking function R then calculate their correlation.<\/p>\n\n<h4>Authentication for Azure AD<\/h4>\n<ul>\n<li style=\"text-align: justify;\"><strong>Interactive<\/strong>: You use your account in AAD to either manually authenticate or obtain an authentication token. Interactive authentication is used during experimentation and iterative development. It enables you to control access to resources (such as a web service) on a per-user bias.<\/li>\n<li style=\"text-align: justify;\"><strong>Service principal:<\/strong> You create a service principal account in AAD, and use it to authenticate or obtain an authentication token. A service principal is used when you need an automated process to authenticate to the service.<\/li>\n<li style=\"text-align: justify;\"><strong>Azure CLI session:<\/strong> Azure CLI authentication is used during experimentation and iterative development, or when you need an automated process to authenticate to the service using a pre-authenticated session.<\/li>\n<li style=\"text-align: justify;\"><strong>Managed identity:\u00a0<\/strong>When using the Azure Machine Learning SDK on an Azure VM, you can use a managed identity for Azure. This workflow allows the VM to connect to the workspace using the managed identity, without storing credential in code or prompting the user to authenticate.<\/li>\n<\/ul>\n<h4><a href=\"https:\/\/learn.microsoft.com\/en-us\/previous-versions\/azure\/machine-learning\/studio-module-reference\/tune-model-hyperparameters\">Tuning Hyper-Parameters<\/a><\/h4>\n<ul>\n<li style=\"text-align: justify;\"><strong>Grid Sampling: <\/strong>All hyper-parameters are discrete<\/li>\n<li style=\"text-align: justify;\"><strong>Random Sampling:<\/strong> Randomly select a value for each hyper-parameter, which can be a mix of discrete and continuous values.<\/li>\n<li style=\"text-align: justify;\"><strong>Bayesian Sampling:<\/strong> Hyper-parameters values are chosen based on the Bayesian optimization algorithm, which tries to select parameter combinations that will result in improved performance from the previous selection.<\/li>\n<\/ul>\n<p>Note: Bayesian Sampling does not support early-termination.<\/p>\n<h4>Differential Privacy<\/h4>\n<p style=\"text-align: justify;\">The amount of variation caused by adding noise is configurable through a parameter called epsilon. This value governs the amount of additional risk that your personal data can be identified through rejecting the opt-out option and participating in a study. The key thing is that it applies this privacy principle for everyone participating in the study. A low epsilon value provides the most privacy, at the expense of less accuracy when aggregating the data. A higher epsilon value results in aggregations that are more true to the actual data distribution, but in which the individual contribution of a single individual to the aggregated value is less obscured by noise.\u00a0<\/p>\n<h4><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/how-to-machine-learning-interpretability?view=azureml-api-2\">Model Interpretability<\/a><\/h4>\n<p>Interpretability helps answer questions in scenarios such as:<\/p>\n<ul>\n<li>Model debugging: Why did my model make this mistake? How can I improve my model?<\/li>\n<li>Human-AI collaboration: How can I understand and trust the model&#8217;s decisions?<\/li>\n<li>Regulatory compliance: Does my model satisfy legal requirements?<\/li>\n<\/ul>\n<p>\u4e0b\u5716\u5c07 Explainer \u53c8\u5206\u6210\u4e09\u5927\u985e\uff1a<\/p>\n<ul>\n<li style=\"text-align: justify;\"><strong>MimicExplainer<\/strong>\u00a0&#8211; An explainer that creates a\u00a0<em>global surrogate model<\/em> that approximates your trained model and can be used to generate explanations. This explainable model must have the same kind of architecture as your trained model (for example, linear or tree-based).<\/li>\n<li style=\"text-align: justify;\"><strong style=\"font-size: inherit; color: var(--text-color); font-family: var(--text-font);\">architecture-appropriate SHAP algorithm<\/strong><span style=\"font-size: inherit; color: var(--text-color); font-family: var(--text-font);\"> &#8211; An explainer that acts as a wrapper around various SHAP explainer algorithms, automatically choosing the one that is most appropriate for your model architecture.<\/span><\/li>\n<li style=\"text-align: justify;\"><strong style=\"font-size: inherit; color: var(--text-color); font-family: var(--text-font);\">PFIExplainer<\/strong><span style=\"font-size: inherit; color: var(--text-color); font-family: var(--text-font);\">\u00a0&#8211; a\u00a0<\/span><em style=\"font-size: inherit; color: var(--text-color); font-family: var(--text-font);\">Permutation Feature Importance<\/em><span style=\"font-size: inherit; color: var(--text-color); font-family: var(--text-font);\">\u00a0explainer that analyzes feature importance by shuffling feature values and measuring the impact on prediction performance.<\/span><\/li>\n<\/ul>\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"959\" src=\"https:\/\/myoceane.fr\/wp-content\/uploads\/2023\/12\/interpretability-architecture-1024x959.png\" alt=\"\" class=\"wp-image-9569\" style=\"width:536px;height:auto\" srcset=\"https:\/\/myoceane.fr\/wp-content\/uploads\/2023\/12\/interpretability-architecture-1024x959.png 1024w, https:\/\/myoceane.fr\/wp-content\/uploads\/2023\/12\/interpretability-architecture-300x281.png 300w, https:\/\/myoceane.fr\/wp-content\/uploads\/2023\/12\/interpretability-architecture-768x719.png 768w, https:\/\/myoceane.fr\/wp-content\/uploads\/2023\/12\/interpretability-architecture.png 1118w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n\n<p>Note: Tabular Explainer \u8ddf Mimic Explainer \u53ef\u4ee5\u89e3\u91cb\u7279\u5fb5\u5728 global \u8207 local \u7684\u91cd\u8981\u6027\uff0c\u4f46\u662f PFI Explainer \u4e0d\u80fd\u89e3\u91cb\u7279\u5fb5\u5728 local \u7684\u91cd\u8981\u6027\u3002<\/p>\n\n<h4><a href=\"https:\/\/learn.microsoft.com\/en-us\/previous-versions\/azure\/machine-learning\/studio-module-reference\/clean-missing-data\">Missing Value Module<\/a><\/h4>\n<p>\u5728\u524d\u8655\u7406\u8a13\u7df4\u8cc7\u6599\u96c6\u7684\u6642\u5019\uff0c\u5f88\u5e38\u6703\u9047\u5230\u8cc7\u6599\u641c\u96c6\u4e0d\u5b8c\u6574\u7684\u6642\u5019\uff0c\u6b64\u6642\u53ef\u4ee5\u9664\u4e86\u53ef\u4ee5\u76f4\u63a5\u5c07 data \u522a\u9664\u4e4b\u5916\uff0c\u4e5f\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u7684\u65b9\u6cd5\u5c07\u8cc7\u6599\u88dc\u9f4a\uff0c\u4ee5\u4e0b\u7c21\u4ecb<a href=\"https:\/\/learn.microsoft.com\/en-us\/previous-versions\/azure\/machine-learning\/studio-module-reference\/clean-missing-data\">\u53c3\u8003\u7db2\u7ad9<\/a>\u88dc\u9f4a\u8cc7\u6599\u7684\u65b9\u6cd5\uff1a<\/p>\n<ul>\n<li style=\"text-align: justify;\"><strong>Replace with MICE: <\/strong>For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as &#8220;Multivariate Imputation using Chained Equations&#8221; or &#8220;Multiple Imputation by Chained Equations&#8221;<\/li>\n<li style=\"text-align: justify;\"><strong>Custom substitute value: <\/strong>\u5ba2\u88fd\u5316\u5e36\u5165\u4e00\u500b\u503c<\/li>\n<li style=\"text-align: justify;\"><strong>Replace with mean: <\/strong>\u4f7f\u7528\u5e73\u5747\u6578<\/li>\n<li style=\"text-align: justify;\"><strong>Replace with median: <\/strong>\u4f7f\u7528\u4e2d\u4f4d\u6578<\/li>\n<li style=\"text-align: justify;\"><strong>Replace with mode: <\/strong>\u4f7f\u7528\u773e\u6578<\/li>\n<li style=\"text-align: justify;\"><strong>Remove entire row: <\/strong>\u76f4\u63a5\u79fb\u9664\u4e00\u5217<\/li>\n<li style=\"text-align: justify;\"><strong>Remove entire column: <\/strong>\u79fb\u9664\u6574\u884c<\/li>\n<li style=\"text-align: justify;\"><strong>Replace with Probabilistic PCA:<\/strong> Replaces the missing values by using a linear model that analyzes the correlations between the columns and estimates a low-dimensional approximation of the data, from which the full data is reconstructed.<\/li>\n<\/ul>\n<p>Note: SMOTE \u4e26\u4e0d\u662f Clean Missing Data \u7684\u6280\u8853\uff0c\u4ed6\u5176\u5be6\u662f\u70ba\u4e86\u8981\u89e3\u6c7a Imbalanced data \u7684\u554f\u984c\u3002<\/p>","protected":false},"excerpt":{"rendered":"<p>\u6ab8\u6aac\u7238\u5728\u8003\u5b8c Microsoft AZ-104 \u4e4b\u5f8c\uff0c\u53c8\u6709\u65b0\u7684\u9700\u6c42\u91dd\u5c0d\u7684\u662f Microsoft DP-100\uff0c\u9019\u500b\u70ba\u60f3\u8981\u6210\u70ba Azure Data Scientist \u7684\u4eba\u8a2d\u8a08\u7684\uff0c\u8207 AZ-104 \u8003\u8a66\u76f8\u540c\uff0c\u5fae\u8edf\u6709\u63d0\u4f9b\u514d\u8cbb\u7684\u6559\u6750\uff0c\u5982\u679c\u6709\u9700\u8981\u9032\u4e00\u6b65\u7684\u6559\u5b78\u4e5f\u6709\u76f8\u5c0d\u61c9\u7684\u8ab2\u7a0b\u53ef\u4ee5\u8cfc\u8cb7\u3002<\/p>\n","protected":false},"author":1,"featured_media":9488,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[1492,1748,1747],"class_list":["post-9256","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-ml","tag-certification","tag-data-scientist","tag-dp-100"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>[\u5fae\u8edf\u8a8d\u8b49] Microsoft Certification DP-100 - \u60f3\u65b9\u6d89\u6cd5 - \u91cf\u74f6\u5916\u7684\u5929\u7a7a M-Y-Oceane<\/title>\n<meta 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