{"id":97869,"date":"2024-11-11T10:52:36","date_gmt":"2024-11-11T05:22:36","guid":{"rendered":"https:\/\/www.whizlabs.com\/blog\/?p=97869"},"modified":"2024-12-04T13:10:29","modified_gmt":"2024-12-04T07:40:29","slug":"aws-ml-workflow-certification-best-practices","status":"publish","type":"post","link":"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/","title":{"rendered":"AWS ML Workflow: Certification Best Practices"},"content":{"rendered":"<p>Understanding the best practices in AWS ML workflows will not only earn you an AWS certification but also enable you to optimize and automate ML models on an advanced level.<\/p>\n<p>This article will explore some of the best practices when working with ML workflows and automation on AWS so that you\u2019re not only successful in certification but also practical applications. Keep reading to find out more.<\/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\/aws-ml-workflow-certification-best-practices\/#How_Machine_Learning_ML_Workflow_on_AWS_Works\" >How Machine Learning (ML) Workflow on AWS Works?<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#Step1_Data_Collection_Preparation\" >Step1: Data Collection &amp; Preparation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#Step2_Model_Training_Evaluation\" >Step2: Model Training &amp; Evaluation<\/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\/aws-ml-workflow-certification-best-practices\/#Step3_Model_Deployment\" >Step3: Model Deployment<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#Step4_Monitoring_Retraining_the_Model\" >Step4: Monitoring &amp; Retraining the Model<\/a><\/li><\/ul><\/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\/aws-ml-workflow-certification-best-practices\/#AWS_ML_Workflow_Best_Practices\" >AWS ML Workflow Best Practices<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#Attaining_Scalability_through_Managed_Services\" >Attaining Scalability through Managed Services<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#Automating_Data_Pipelines\" >Automating Data Pipelines<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#Cost_Optimization\" >Cost Optimization<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#Monitoring_and_Retraining_Models\" >Monitoring and Retraining Models<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#Leveraging_SageMaker_Pipelines\" >Leveraging SageMaker Pipelines<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#10_Best_Practices_for_AWS_ML_Certification\" >10 Best Practices for AWS ML Certification<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#Take_a_look_at_the_Exam_Guide\" >Take a look at the Exam Guide<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#Create_a_Study_Plan\" >Create a Study Plan<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#Focus_on_Key_Concepts\" >Focus on Key Concepts<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#Hands-On_Experience\" >Hands-On Experience<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#Automation_of_Workflows\" >Automation of Workflows<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#Optimize_for_Cost\" >Optimize for Cost<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#Master_Data_Management\" >Master Data Management<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#Leverage_AWS_training_resources\" >Leverage AWS training resources<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#Study_Documentation_and_Questions\" >Study Documentation and Questions<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#Case_Studies\" >Case Studies<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-ml-workflow-certification-best-practices\/#Conclusion\" >Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"How_Machine_Learning_ML_Workflow_on_AWS_Works\"><\/span><strong>How Machine Learning (ML) Workflow on AWS Works?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">To understand Machine Learning Workflow on AWS, there are several steps one needs to be conversant with for a model to be transformed from a concept to production. These steps include:<br \/>\n<img decoding=\"async\" class=\"alignnone wp-image-97898 size-full\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/11\/how-machine-learning-workflow-on-aws-works.webp\" alt=\"how machine learning workflow on aws works\" width=\"1536\" height=\"859\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/11\/how-machine-learning-workflow-on-aws-works.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/11\/how-machine-learning-workflow-on-aws-works-300x168.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/11\/how-machine-learning-workflow-on-aws-works-1024x573.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/11\/how-machine-learning-workflow-on-aws-works-768x430.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/11\/how-machine-learning-workflow-on-aws-works-150x84.webp 150w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/><br \/>\n<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Step1_Data_Collection_Preparation\"><\/span><strong>Step1: Data Collection &amp; Preparation<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Data Collection is important because AWS services such as AWS Glue &amp; S3 are specifically used for storage and preparation of large quantities of data. AWS Glue together with Amazon SageMaker Data Wrangler also used to clean, transform and normalize the data collected into models you can train.<br \/>\nS3 serves your best as your central data repository because it will ensure scalability and security. It is recommended you partition or organize your data into folders named by category or period for easy access and querying. Also, use Amazon Kinesis for streamline data, which serves best in real-time data ingestion.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Step2_Model_Training_Evaluation\"><\/span><strong>Step2: Model Training &amp; Evaluation<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Once your data has been normalized, it\u2019s time to train and evaluate the models you\u2019ll be working on. Amazon SageMaker is categorically used for this purpose.<br \/>\nYou\u2019ll be able to train models at scale through the in-built algorithms or you can customize your codes in alternative frameworks such as TensorFlow, Scikit-learn or PyTorch depending on what you\u2019re comfortable with.<br \/>\nOnce you\u2019ve trained your model, you need to validate its performance through testing it against the test data using AWS tools. There are performance metrics that your model must meet at this stage for you to proceed to model deployment.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Step3_Model_Deployment\"><\/span><strong>Step3: Model Deployment<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Once you\u2019ve texted and evaluated your model against the relevant performance metrics, it\u2019s time to deploy it. You can choose between SageMaker or Lambda for low latency and real-time inference.<br \/>\nAutoscaling for SageMaker endpoints that is based on traffic patterns ensures cost-effectiveness and excellent performance for your models. SageMaker Batch Transform will come in handy when making predictions on large datasets for cases that don\u2019t require real-time prediction.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Step4_Monitoring_Retraining_the_Model\"><\/span><strong>Step4: Monitoring &amp; Retraining the Model<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>After deployment, you need to monitor the model\u2019s performance so that it\u2019s up to date with its functions and from time to time you\u2019ll need to retrain it with AWS services such as Amazon CloudWatch &amp; SageMaker Model Monitor.<br \/>\nSageMaker Multi-Model Endpoints will be essential in managing multiple model versions with testing. Encrypt your data that is at rest and in transit with AWS Key Management Service (KMS). Use AWS Identity and Access Management (IAM) to restrict unauthorized access to models, datasets and ML resources.<br \/>\nThe end-to-end process is fundamental to anyone trying to learn machine learning certification and it\u2019s extensively covered under AWS certification examinations. Leverage AWS services that are compliant with cloud-based computing industry standards such as GDPR, HIPAA &amp; SOC especially when dealing with sensitive data.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"AWS_ML_Workflow_Best_Practices\"><\/span><strong>AWS ML Workflow Best Practices<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Here are some of the best practices you can follow when designing and implementing AWS ML Workflow.<\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-97882 size-full\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/11\/aws-ml-workflow-best-practices.webp\" alt=\"aws ml workflow best practices\" width=\"1536\" height=\"859\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/11\/aws-ml-workflow-best-practices.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/11\/aws-ml-workflow-best-practices-300x168.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/11\/aws-ml-workflow-best-practices-1024x573.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/11\/aws-ml-workflow-best-practices-768x430.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/11\/aws-ml-workflow-best-practices-150x84.webp 150w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Attaining_Scalability_through_Managed_Services\"><\/span><strong>Attaining Scalability through Managed Services<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Scalability is important when working with <strong><a title=\"ML Workflow\" href=\"https:\/\/aws.amazon.com\/tutorials\/machine-learning-tutorial-mlops-automate-ml-workflows\/\" target=\"_blank\" rel=\"nofollow noopener\">ML Workflow<\/a><\/strong> and AWS provides managed services through platforms such as the Amazon SageMaker so that you\u2019re able to automate many parts of your model.<br \/>\nYour focus will be on model development &amp; deployment since infrastructure concerns will have been eliminated. Managed services will automatically compute and store resources for training thereby saving your time and reducing your resources.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Automating_Data_Pipelines\"><\/span><strong>Automating Data Pipelines<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Using AWS Glue, you can automate data pipelines during data preparation. This is significant because AWS Glue will help you transform, catalog and seamlessly integrate with AWS S3 and other similar services. Also, AWS Glue will automatically detect data schema and create ETL jobs ensuring up-to-date training of your models.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Cost_Optimization\"><\/span><strong>Cost Optimization<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Running large scale machine learning activities are normally costly. However, AWS allows you to use Spot Instances provided by SageMaker which enables scaling of resources only when they\u2019re deemed necessary. With Spot Training you\u2019ll be able to save upto 90% on your training costs.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Monitoring_and_Retraining_Models\"><\/span><strong>Monitoring and Retraining Models<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Machine learning models do degrade over time because data is continuous in nature. AWS SageMaker Model monitor lets you keep your models updated with the recent data by easily detecting a shift in data and performance degradation. The model monitor tracks the changes in your model accuracy and triggers retraining prompts to stay on track with the latest data.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Leveraging_SageMaker_Pipelines\"><\/span><strong>Leveraging SageMaker Pipelines<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>AWS SageMaker Pipelines<\/strong> automate each stage of machine learning in terms of management, building and end-to-end workflows. Creating automated pipelines makes handling your data during preprocessing much faster, your training much easier and a streamlined deployment of repetitive tasks to reduce human error.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"10_Best_Practices_for_AWS_ML_Certification\"><\/span><strong>10 Best Practices for AWS ML Certification<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>AWS Machine Learning certification requires huge preparation, hands-on experience, and deep expertise in how AWS services will integrate within the workflow of machine learning. This section will indicate some of the key best practices to be followed for good preparations and give some valuable tips:<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Take_a_look_at_the_Exam_Guide\"><\/span><strong>Take a look at the Exam Guide<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>AWS provides an exam guide that gives five knowledge domains on which the exam is based. Those include data engineering, exploratory data analysis, modelling, and machine learning implementation and operation. Be sure to study all the topics listed in the guide for proper coverage.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Create_a_Study_Plan\"><\/span><strong>Create a Study Plan<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Organize your learning with a structured study plan. Divide the topics into comfortable sections, containing both theoretical learning and practical application that will help you bring in everything necessary for the examination.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Focus_on_Key_Concepts\"><\/span><strong>Focus on Key Concepts<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>The exam will actually test deep knowledge about some very important concepts in machine learning &#8211; namely, model training, tuning, deployment, and monitoring. Also, give special attention to AWS-specific features for managing machine learning life cycles using SageMaker, Glue, and CloudWatch.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Hands-On_Experience\"><\/span><strong>Hands-On Experience<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Gaining real-world experience is crucial. Practice on live projects with the main AWS services: Amazon SageMaker for model building, AWS Glue for data preparation, and Amazon S3 to store your data.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Automation_of_Workflows\"><\/span><strong>Automation of Workflows<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>You should be set to automate workflows since this is one of the most key roles machine learning will play at AWS. You have to be comfortable driving the setup of ML pipelines and automating ETL workflows with services such as SageMaker and AWS Glue. It is expected that, by hands-on knowledge, you know how to automate such workflows, which likely will form part of the exams in the certification.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Optimize_for_Cost\"><\/span><strong>Optimize for Cost<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>AWS certification places much emphasis on such development of cost optimization. You are expected to understand resource allocation efficiency, especially on service resources like EC2 Spot Instances and Auto Scaling. Scoring in this section demands an understanding of when and how to use the techniques that save costs.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Master_Data_Management\"><\/span><strong>Master Data Management<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Your skills in data management should be unparalleled. You will learn to effectively use Amazon S3, AWS Glue, and SageMaker Data Wrangler in handling and preparing data for machine learning workflows. Since Data Engineering is an essential section of the certification, focus on the understanding of how these services will work together to build a reliable pipeline.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Leverage_AWS_training_resources\"><\/span><strong>Leverage AWS training resources<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>AWS provides both free and paid training courses that are directly aimed at setting candidates up for success in their certification. Use those resources, as they have a lot to say about the structure and the contents of the exam.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Study_Documentation_and_Questions\"><\/span><strong>Study Documentation and Questions<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>In fact, most services are documented with how they are applied in real life, along with best practices for implementing ML workflows.<br \/>\nAWS and other third-party sites provide sample questions and practice exams that emulate the actual certification test. Working through these will help you become familiar with the types of questions and the level of difficulty you can expect.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Case_Studies\"><\/span><strong>Case Studies<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Go through AWS case studies as related to machine learning to understand how it is actually used in the field. That gives a good view on how AWS ML services are actually implemented in real-world work processes and provides insight that will be helpful not only during the exam, but even in real-world implementations.<br \/>\nNote: A pointed concentration on hands-on experience with AWS services, besides bringing in the best practices, will help someone prepare well to ace the AWS Machine Learning certification exam in one attempt.<\/p>\n<h4><strong>Case Study: Real-World Example<\/strong><\/h4>\n<p>The below section showcases a hypothetical case study to illustrate the ML workflow on AWS:<br \/>\nA healthcare organization aims to predict the readmission of patients by utilizing electronic health records (EHRs). The steps include the following:<\/p>\n<h4><strong>Data Collection &amp; Preparation<\/strong><\/h4>\n<p>Collection of the required data from the EHR system of the organization and transmitting it to Amazon S3, the scalable storage solution of AWS. Examples of such data include but are not limited to: patient demographics, medical history, lab test results, and past admissions in the hospitals. Since healthcare-related data usually is incomplete or inconsistent, cleaning and preprocessing of such data should be performed by the organization.<br \/>\nThis involves the handling of missing values, usually via imputation, and normalization of features, which may be in the form of scaling numeric data or encoding categorical variables. In essence, such preparation of data improves the quality and suitability of data to a large extent for training models.<\/p>\n<h4><strong>Model Development and Training<\/strong><\/h4>\n<p>With data prepared, the company then uses Amazon SageMaker as a powerful ML development tool to build and train the predictive model. Any variety of algorithms could be considered, such as either a random forest or GBMs to provide only two common examples; both methods are appropriate for classification problems.<br \/>\n<strong>ML with Amazon <\/strong><strong>SageMaker<\/strong> now allows the healthcare team to try various models, tune hyperparameters, and assess various performance metrics, such as accuracy, precision, and recall. After all, this step will make sure the model fits well for the problematic prediction of patient readmissions using historical data.<\/p>\n<h4><strong>Model Deployment<\/strong><\/h4>\n<p>After the model has been trained and tuned, the deployment capability of SageMaker will be used, deploying the model as an endpoint. This step of deployment allows the model to be accessible for real-time predictions. The deployed model will be provided scalable infrastructure by SageMaker that would handle a load variety and provide results within a short time without explicitly managing any servers.<\/p>\n<h4><strong>Model Serving<\/strong><\/h4>\n<p>In any new admission, the healthcare team can send the data of the patient to the SageMaker endpoint, which will process the input data and predict the likelihood of readmission from the model. The predictions have the potential to inform clinical decisions, so enabling health care providers to focus on high-risk patients might allow them to adjust care plans with the possibility of preventing readmissions.<\/p>\n<h4><strong>Model Monitoring<\/strong><\/h4>\n<p>Continuous monitoring keeps the model valid and relevant over time. AWS services, such as Amazon CloudWatch and SageMaker Model Monitor, provide the organization with the ability to monitor real-time performance of the model.<br \/>\nIf the model starts showing any signs of the beginning of concept drift-a situation where the relationship between the input data and the forecasted output changes-there are the alerts that trigger a retraining of the model. This keeps predictions reliable in the case of either a change in the patient population or healthcare practices.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><strong>Conclusion<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Mastering ML Workflow on AWS is an important step in your career because it increases your prospects in cloud-based machine learning and optimization of AWS in providing scalability, automation and cost-effectiveness during deployment of models. By following this guide, you\u2019ll have the essential information for real-world applications which is a core part of AWS machine learning specialty exams.<\/p>\n<p>Hands-on experience is expected before taking the <strong><a title=\"AWS Certified Machine Learning Specialty\" href=\"https:\/\/www.whizlabs.com\/aws-certified-machine-learning-specialty\/\" target=\"_blank\" rel=\"noopener\">AWS Certified Machine Learning\u00a0Specialty<\/a> <\/strong>or <strong><a title=\"AWS Certified Machine Learning Engineer Associate exam\" href=\"https:\/\/www.whizlabs.com\/aws-certified-machine-learning-engineer-associate\/\" target=\"_blank\" rel=\"noopener\">AWS Certified Machine Learning Engineer Associate exam<\/a><\/strong>. Get hands-on directly with the no-risk <strong><a title=\"AWS Sandbox\" href=\"https:\/\/www.whizlabs.com\/aws-sandbox\/\" target=\"_blank\" rel=\"noopener\">AWS Sandbox<\/a><\/strong> boundary, free to use the AWS service to build, train, and then deploy different machine learning models.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Understanding the best practices in AWS ML workflows will not only earn you an AWS certification but also enable you to optimize and automate ML models on an advanced level. This article will explore some of the best practices when working with ML workflows and automation on AWS so that you\u2019re not only successful in certification but also practical applications. Keep reading to find out more. How Machine Learning (ML) Workflow on AWS Works? To understand Machine Learning Workflow on AWS, there are several steps one needs to be conversant with for a model to be transformed from a concept [&hellip;]<\/p>\n","protected":false},"author":438,"featured_media":97891,"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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This article will explore some of the best practices when working with ML workflows and automation on AWS so that you\u2019re not only successful in&hellip;","_links":{"self":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/97869","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=97869"}],"version-history":[{"count":14,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/97869\/revisions"}],"predecessor-version":[{"id":98324,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/97869\/revisions\/98324"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media\/97891"}],"wp:attachment":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media?parent=97869"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/categories?post=97869"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/tags?post=97869"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}