{"id":98535,"date":"2025-01-15T17:54:19","date_gmt":"2025-01-15T12:24:19","guid":{"rendered":"https:\/\/www.whizlabs.com\/blog\/?p=98535"},"modified":"2025-03-26T16:15:01","modified_gmt":"2025-03-26T10:45:01","slug":"model-outcomes-aws-ml-performance-tools","status":"publish","type":"post","link":"https:\/\/www.whizlabs.com\/blog\/model-outcomes-aws-ml-performance-tools\/","title":{"rendered":"How to Improve Model Outcomes with AWS ML Performance Tools?"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">This blog discusses the AWS services and tools that can help improve model performance and outcomes with relevance to <a title=\"AWS Certified Machine Learning Engineer-Associate (MLA-C01)\" href=\"https:\/\/www.whizlabs.com\/aws-certified-machine-learning-engineer-associate\/\" target=\"_blank\" rel=\"noopener\"><strong>AWS Certified Machine Learning Engineer-Associate (MLA-C01)<\/strong><\/a> certification. You can automate performance tracking, anomaly detection, and real-time debugging using these tools. Read through this blog to ensure the model remains accurate, reliable, and aligned with business goals, even in dynamic environments.<\/span><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_76 ez-toc-wrap-left counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #ea7e02;color:#ea7e02\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #ea7e02;color:#ea7e02\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.whizlabs.com\/blog\/model-outcomes-aws-ml-performance-tools\/#AWS_Certified_Machine_Learning_Engineer-Associate_MLA-C01_overview\" >AWS Certified Machine Learning Engineer-Associate (MLA-C01) overview<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.whizlabs.com\/blog\/model-outcomes-aws-ml-performance-tools\/#Best_Practices_for_Improving_Model_Outcomes_with_AWS\" >Best Practices for Improving Model Outcomes with AWS<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.whizlabs.com\/blog\/model-outcomes-aws-ml-performance-tools\/#Gather_high-quality_data\" >Gather high-quality data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.whizlabs.com\/blog\/model-outcomes-aws-ml-performance-tools\/#Detect_analyze_and_alert\" >Detect, analyze, and alert<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.whizlabs.com\/blog\/model-outcomes-aws-ml-performance-tools\/#Automate_ML_for_consistency\" >Automate ML for consistency<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.whizlabs.com\/blog\/model-outcomes-aws-ml-performance-tools\/#Define_relevant_evaluation_metrics\" >Define relevant evaluation metrics<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.whizlabs.com\/blog\/model-outcomes-aws-ml-performance-tools\/#Implement_model_monitoring\" >Implement model monitoring<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.whizlabs.com\/blog\/model-outcomes-aws-ml-performance-tools\/#Optimization_best_practices\" >Optimization best practices<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.whizlabs.com\/blog\/model-outcomes-aws-ml-performance-tools\/#Final_thoughts\" >Final thoughts<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"AWS_Certified_Machine_Learning_Engineer-Associate_MLA-C01_overview\"><\/span><strong>AWS Certified Machine Learning Engineer-Associate (MLA-C01) overview<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Launched in June 2024,\u00a0 the <\/span><span style=\"font-weight: 400;\">AWS Certified Machine Learning Engineer-Associate (MLA-C01)<\/span><span style=\"font-weight: 400;\"> exam, is one of the newest AWS certifications that aligns with the evolving needs of the industry and ML engineers. The MLA-C01 exam checks your ability to create, deploy, and manage machine learning solutions and workflows on AWS.\u00a0 To take exam, you must have at least one year of experience in ML or a related field. A thorough knowledge of all stages of the ML lifecycle will help you pass the exam:<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-98553 size-full\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/mastering-the-ml-lifecycle-key-stages-to-ace-your-exam.webp\" alt=\"mastering the ml lifecycle key stages to ace your exam\" width=\"1536\" height=\"543\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/mastering-the-ml-lifecycle-key-stages-to-ace-your-exam.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/mastering-the-ml-lifecycle-key-stages-to-ace-your-exam-300x106.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/mastering-the-ml-lifecycle-key-stages-to-ace-your-exam-1024x362.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/mastering-the-ml-lifecycle-key-stages-to-ace-your-exam-768x272.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/mastering-the-ml-lifecycle-key-stages-to-ace-your-exam-150x53.webp 150w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">The primary focus of this exam is Amazon SageMaker (now renamed as Amazon SageMaker AI) and its different features and tools for MLOps.\u00a0 As an ML engineer, the course requires you to have more practical experience and delve deep into the settings and configurations of AWS ML services and tools.\u00a0 In this blog, we will focus on the best practices for evaluating and improving the performance of ML models using appropriate metrics. These best practices are predominantly covered in domains 2 and 4 of the <\/span><strong><a title=\"exam guide\" href=\"https:\/\/d1.awsstatic.com\/training-and-certification\/docs-machine-learning-engineer-associate\/AWS-Certified-Machine-Learning-Engineer-Associate_Exam-Guide.pdf\" target=\"_blank\" rel=\"nofollow noopener\">exam guide<\/a><\/strong><span style=\"font-weight: 400;\"> and are based on AWS Well-Architected Machine ML Lens<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The following table shows how evaluation and monitoring topics are covered in domains 2 and 4 in the\u00a0 AWS Certified Machine Learning Engineer-Associate exam course.<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-98549 size-full\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/evaluation-and-monitoring-in-aws-certified-machine-learning-engineer-exam.webp\" alt=\"evaluation and monitoring in aws certified machine learning engineer exam\" width=\"1536\" height=\"1050\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/evaluation-and-monitoring-in-aws-certified-machine-learning-engineer-exam.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/evaluation-and-monitoring-in-aws-certified-machine-learning-engineer-exam-300x205.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/evaluation-and-monitoring-in-aws-certified-machine-learning-engineer-exam-1024x700.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/evaluation-and-monitoring-in-aws-certified-machine-learning-engineer-exam-768x525.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/evaluation-and-monitoring-in-aws-certified-machine-learning-engineer-exam-150x103.webp 150w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">The exam questions are scenario-based, requiring you to think about which evaluation and monitoring metrics to use to measure the performance of a certain type of model.\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Best_Practices_for_Improving_Model_Outcomes_with_AWS\"><\/span><strong>Best Practices for Improving Model Outcomes with AWS<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">The ML lifecycle is an iterative process where each stage directly impacts the model\u2019s outcomes. The lifecycle is the end-to-end process for developing, deploying, and maintaining ML models.\u00a0 Before developing the ML model, plan improvement drivers for optimizing model performance. Examples of improvement drivers include: collecting more data, cross-validation, feature engineering, tuning hyperparameters, and ensemble methods.<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-98555 size-full\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/ml-lifecycle.webp\" alt=\"ml lifecycle\" width=\"1536\" height=\"543\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/ml-lifecycle.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/ml-lifecycle-300x106.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/ml-lifecycle-1024x362.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/ml-lifecycle-768x272.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/ml-lifecycle-150x53.webp 150w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Here are some best practices to optimize the entire lifecycle to improve model outcomes:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Gather high-quality data\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Automate ML for consistency<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Define relevant evaluation metrics<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Implement model monitoring<\/span><\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Gather_high-quality_data\"><\/span><span style=\"font-weight: 400;\">Gather high-quality data<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">ML models are only as good as the data that is used to train them. Ensure clean, well-labeled, and diverse data is used for training.\u00a0<\/span><\/p>\n<p><strong>The following AWS services help you create high-quality quality:<\/strong><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>SageMaker Data Wrangler:<\/strong> explore and import data from a variety of popular sources and transform that data into a structured format within the a single pipeline. The 300 built-in data transformations enables you to normalize, transform, and combine features without having to write any code.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>SageMaker Ground Truth:<\/strong> combine manual and automated data labelling to produce accurate, high-quality training datasets<\/span><\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Detect_analyze_and_alert\"><\/span><span style=\"font-weight: 400;\">Detect, analyze, and alert<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Monitor training progress and detect anomalies while training or retraining. AWS provides the following two tools:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>SageMaker Debugger:\u00a0<\/strong> debug training jobs and resolve issues to improve the performance of your model. Send alerts when training anomalies are found, take actions against the issues, and identify the root cause of them by visualizing collected metrics.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Amazon SageMaker with TensorBoard:<\/strong> use the visualization tools of\u00a0TensorBoard\u00a0in\u00a0 SageMaker to analyze the overall training progress and trends.<\/span><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Automate_ML_for_consistency\"><\/span><strong>Automate ML for consistency<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Manual ML is prone to error, inconstancy, and oversight, which can negatively impact model outcomes. You can use different SageMaker automation features to improve consistency and model quality, thereby delivering accurate outcomes.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>SageMaker Canvas:<\/strong> use a UI-based platform for no-code AutoML experience<\/span><b>.<\/b><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><strong>SageMaker Autopilot: <\/strong><span style=\"font-weight: 400;\">automate the end-to-end process of building, training, tuning, and deploying machine learning models.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>SageMaker JumpStart:<\/strong> use pretrained, open-source models for various problem types to help you get started with ML.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>MLflow with SageMaker:<\/strong> create, manage, analyze, and compare your machine learning experiments to gain insights to deploy your best-performing models<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SageMaker <\/span><a title=\"built-in algorithms\" href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/algos.html\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\"><strong>built-in algorithms<\/strong><\/span><\/a><span style=\"font-weight: 400;\">, pre-trained models, and pre-built solution templates: trainand deploy machine learning models quickly.<\/span><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Define_relevant_evaluation_metrics\"><\/span><strong>Define relevant evaluation metrics<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">To<\/span> <span style=\"font-weight: 400;\">evaluate how well a model performs on a given task, establish metrics that directly relate to the KPIs that are established in the business goal identification phase. Evaluate the metrics with consideration to the real-world use cases to maximize business value. However, selecting the right performance metric is crucial since different metrics highlight different aspects of model performance and different machine-learning tasks have different performance metrics. Different SageMaker tools provide built-in and customizable metrics for both supervised and unsupervised ML learning tasks. These metrics make it easier to evaluate different ML model, tasks, or iterations.\u00a0<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-98548 size-full\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/different-sagemaker-tools.webp\" alt=\"\" width=\"1536\" height=\"288\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/different-sagemaker-tools.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/different-sagemaker-tools-300x56.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/different-sagemaker-tools-1024x192.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/different-sagemaker-tools-768x144.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/different-sagemaker-tools-150x28.webp 150w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>SageMaker Clarify:<\/strong> evaluates ML models, detect bias, and explain model predictions. Clarify can be used in in pre-training data or post-training data that can emerge during training or when your model is in production.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>SageMaker Canvas:<\/strong> provides overview and scoring information for the different types of model..The Canvas leaderboard allows you to compare key performance metrics (for example, accuracy, precision, recall, and F1 score) to identify the best model for your data.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>SageMaker AutoPilot:<\/strong> produces <\/span><strong><a title=\"metrics\" href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/autopilot-metrics-validation.html\" target=\"_blank\" rel=\"nofollow noopener\">metrics<\/a><\/strong><span style=\"font-weight: 400;\"> that measure the predictive quality of machine learning model candidates. SageMaker Autopilot Model Quality Reports generates reports of\u00a0 your model\u2019s metrics to provide visibility into your model\u2019s performance for regression and classification problems.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Amazon Bedrock:<\/strong> evaluate the performance and effectiveness of Amazon Bedrock models and knowledge bases. Bedrock offers three evaluation methods: LLM-as-a-judge, programmatic, and human evaluation.<\/span><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Implement_model_monitoring\"><\/span><strong>Implement model monitoring<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">After you\u2019ve deployed the model, you must continuously\u00a0monitor the performance of the models\u00a0and their success in production. The model monitoring system must capture data, compare that data to the training set, define rules to detect issues, and send alerts.\u00a0The issues detected in the monitoring phase include:\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data quality<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Model quality<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Bias drift<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Feature attribution drift<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Without timely correction, these issues can lead to performance degradation or serious compliance issues. Here are some examples of AWS services and tools for model monitor:<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-98547 size-full\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/aws-services-and-tools-for-model-monitor.webp\" alt=\"aws services and tools for model monitor\" width=\"1536\" height=\"411\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/aws-services-and-tools-for-model-monitor.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/aws-services-and-tools-for-model-monitor-300x80.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/aws-services-and-tools-for-model-monitor-1024x274.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/aws-services-and-tools-for-model-monitor-768x206.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/aws-services-and-tools-for-model-monitor-150x40.webp 150w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>SageMaker Debugger:<\/strong> get insights during training to identify and resolve issues.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>SageMaker Model Monitor:<\/strong> check monitoring reports on deployed models for data drift and performance degradation, with features to automatically retrain models if necessary. Model Monitor is integrated with Amazon SageMaker Clarify to help you identify potential bias drift\u00a0\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Amazon CloudWatch:<\/strong> get metrics and alarms to monitor the performance of your ML models<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>CloudWatch Logs:<\/strong> monitor logs generated by SageMaker endpoints, training jobs, and other infrastructure<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Amazon EventBridge:<\/strong> automate responses to events generated by your ML workflows<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>AWS CloudTrail:<\/strong> track API calls and user activities across AWS ML workflows<\/span><\/li>\n<\/ul>\n<p><strong>AWS ML optimization tools and strategies<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Pre-deployment evaluation and post-deployment monitoring are important strategies for model optimization which involves refining the model. The goal is to enhance the model\u2019s accuracy, efficiency, and ability to generalize well to new data. After calculating the evaluation metrics, you can optimize the model by iterating the previous steps\u00a0in ML lifecycle. AWS provides several tools and features for this iterative optimization process.\u00a0<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-98546 size-full\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/aws-ml-optimization-tools-and-strategies.webp\" alt=\"aws ml optimization tools and strategies\" width=\"1536\" height=\"600\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/aws-ml-optimization-tools-and-strategies.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/aws-ml-optimization-tools-and-strategies-300x117.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/aws-ml-optimization-tools-and-strategies-1024x400.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/aws-ml-optimization-tools-and-strategies-768x300.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/aws-ml-optimization-tools-and-strategies-150x59.webp 150w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Optimization_best_practices\"><\/span><span style=\"font-weight: 400;\">Optimization best practices<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Here are some common optimization best practices to improve model outcomes.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Retrain model<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Create tracking and version ocontrol mechanisms<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Tune hyperparameters<\/span><\/li>\n<\/ul>\n<p><b>Retrain model<\/b><\/p>\n<p><span style=\"font-weight: 400;\">As machines\u2019 operating modes and health change over time leading to\u00a0<\/span><strong>data drift<\/strong><span style=\"font-weight: 400;\">, models must be retrained to take more recent information such as data and labels. Use the following AWS tools:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SageMaker AI Pipeline: create a retrain pipeline and automate\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AWS Step Functions: define all the steps in the retraining work\ufb02ow and set up alerts to automate retraining. Step Functions integrates with EventBridge, allowing you to start an AWS Step Function workflow to initiate retraining tasks in the training pipeline<\/span><b>.\u00a0<\/b><\/li>\n<\/ul>\n<p><b>Create tracking and version control mechanisms<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Log and track your model so that if something goes wrong with a newly deployed version, you can roll back to the latest safe version. Use the following AWS tools:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Amazon SageMaker AI Experiments:<\/strong> automatically tracks the inputs, parameters, configurations, and results of your iterations as\u00a0runs<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Use SageMaker AI Model Registry:<\/strong> stores, manages, and tracks machine learning models<\/span><\/li>\n<\/ul>\n<p><b>Tune hyperparameters<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Hyperparameters can find the best combination of hyperparameters to increase model performance. You can use different hyperparameter tuning strategies with SageMaker.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SageMaker AI automatic model tuning: finds the optimal model by running many training jobs on your dataset. It uses the algorithm and ranges of hyperparameters that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by a metric that you choose.<\/span><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Final_thoughts\"><\/span><strong>Final thoughts<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Creating an ML model that delivers outcomes matching your needs involves a continuous, iterative process encompassing all phases of the ML lifecycle and workflows. Your first model might not even deliver accurate predictive results; you might have to try out a few more variations. The AWS Certified Machine Learning Engineer-Associate equips you with the skills to handle this iterative process to refine models until you achieve predictive accuracy and real-world performance. The exam evaluates your practical skills in implementing AWS performance tools. Sign up for the Whizlabs<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>AWS Certified Machine Learning Engineer Associate course<\/strong><\/span><span style=\"font-weight: 400;\"> to earn this certificate. For additional hands-on experience with the services, check <\/span><strong><a title=\"AWS hands-on labs\" href=\"https:\/\/www.whizlabs.com\/labs\/library\/\" target=\"_blank\" rel=\"noopener\">AWS hands-on labs<\/a><\/strong><span style=\"font-weight: 400;\"> and <\/span><a title=\"AWS sandboxes\" href=\"https:\/\/www.whizlabs.com\/labs\/sandbox\/aws\/aws-sandbox\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\"><strong>AWS sandboxes<\/strong><\/span><\/a><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This blog discusses the AWS services and tools that can help improve model performance and outcomes with relevance to AWS Certified Machine Learning Engineer-Associate (MLA-C01) certification. You can automate performance tracking, anomaly detection, and real-time debugging using these tools. Read through this blog to ensure the model remains accurate, reliable, and aligned with business goals, even in dynamic environments. AWS Certified Machine Learning Engineer-Associate (MLA-C01) overview Launched in June 2024,\u00a0 the AWS Certified Machine Learning Engineer-Associate (MLA-C01) exam, is one of the newest AWS certifications that aligns with the evolving needs of the industry and ML engineers. The MLA-C01 exam [&hellip;]<\/p>\n","protected":false},"author":438,"featured_media":98545,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"default","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[4],"tags":[184,2311,3040],"class_list":["post-98535","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-aws-certifications","tag-aws","tag-aws-machine-learning","tag-aws-ml"],"uagb_featured_image_src":{"full":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/how-to-improve-model-outcomes-with-aws-ml-performance-tools.webp",1536,864,false],"thumbnail":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/how-to-improve-model-outcomes-with-aws-ml-performance-tools-150x150.webp",150,150,true],"medium":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/how-to-improve-model-outcomes-with-aws-ml-performance-tools-300x169.webp",300,169,true],"medium_large":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/how-to-improve-model-outcomes-with-aws-ml-performance-tools-768x432.webp",768,432,true],"large":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/how-to-improve-model-outcomes-with-aws-ml-performance-tools-1024x576.webp",1024,576,true],"1536x1536":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/how-to-improve-model-outcomes-with-aws-ml-performance-tools.webp",1536,864,false],"2048x2048":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/how-to-improve-model-outcomes-with-aws-ml-performance-tools.webp",1536,864,false],"profile_24":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/how-to-improve-model-outcomes-with-aws-ml-performance-tools-24x24.webp",24,24,true],"profile_48":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/how-to-improve-model-outcomes-with-aws-ml-performance-tools-48x48.webp",48,48,true],"profile_96":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/how-to-improve-model-outcomes-with-aws-ml-performance-tools-96x96.webp",96,96,true],"profile_150":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/how-to-improve-model-outcomes-with-aws-ml-performance-tools-150x150.webp",150,150,true],"profile_300":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/how-to-improve-model-outcomes-with-aws-ml-performance-tools-300x300.webp",300,300,true],"tptn_thumbnail":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/how-to-improve-model-outcomes-with-aws-ml-performance-tools-250x250.webp",250,250,true],"web-stories-poster-portrait":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/how-to-improve-model-outcomes-with-aws-ml-performance-tools-640x853.webp",640,853,true],"web-stories-publisher-logo":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/how-to-improve-model-outcomes-with-aws-ml-performance-tools-96x96.webp",96,96,true],"web-stories-thumbnail":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2025\/01\/how-to-improve-model-outcomes-with-aws-ml-performance-tools-150x84.webp",150,84,true]},"uagb_author_info":{"display_name":"Banu Sree Gowthaman","author_link":"https:\/\/www.whizlabs.com\/blog\/author\/banu-sree\/"},"uagb_comment_info":23,"uagb_excerpt":"This blog discusses the AWS services and tools that can help improve model performance and outcomes with relevance to AWS Certified Machine Learning Engineer-Associate (MLA-C01) certification. You can automate performance tracking, anomaly detection, and real-time debugging using these tools. Read through this blog to ensure the model remains accurate, reliable, and aligned with business goals,&hellip;","_links":{"self":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/98535","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/users\/438"}],"replies":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/comments?post=98535"}],"version-history":[{"count":12,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/98535\/revisions"}],"predecessor-version":[{"id":98559,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/98535\/revisions\/98559"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media\/98545"}],"wp:attachment":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media?parent=98535"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/categories?post=98535"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/tags?post=98535"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}