{"id":169031,"date":"2023-07-13T16:37:26","date_gmt":"2023-07-13T15:37:26","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=169031"},"modified":"2023-10-30T19:41:08","modified_gmt":"2023-10-30T18:41:08","slug":"aws-sagemaker-a-guide-for-using-the-platform","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/aws-sagemaker-a-guide-for-using-the-platform","title":{"rendered":"AWS SageMaker: A guide for using the platform"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"169031\" class=\"elementor elementor-169031\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-169ac4f e-flex e-con-boxed e-con\" data-id=\"169ac4f\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8482a8c elementor-widget elementor-widget-heading\" data-id=\"8482a8c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Amazon SageMaker is a cloud platform developed by Amazon Web Services (AWS) that is particularly useful for data science. Below you can learn more about the main features and how to use the tools of the platform from the development of a machine learning model to its deployment.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-14dc111 e-flex e-con-boxed e-con\" data-id=\"14dc111\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8189849 elementor-widget elementor-widget-heading\" data-id=\"8189849\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Introduction to Amazon SageMaker <\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ab6a06a elementor-widget elementor-widget-text-editor\" data-id=\"ab6a06a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><span style=\"font-weight: 400\">Amazon SageMaker is a Machine Learning based cloud platform that is a service of the <a href=\"https:\/\/aws.amazon.com\/fr\/\" target=\"_blank\" rel=\"noopener\">Amazon Web Services (AWS)<\/a> cloud platform. <a href=\"https:\/\/datascientest.com\/en\/5-aws-launches-and-announcements-making-developers-life-easy-in-2022\" target=\"_blank\" rel=\"noopener\">AWS<\/a> has numerous services on the Internet and can be used to manage a cloud application.<\/span><\/p><p><span style=\"font-weight: 400\">SageMaker allows a user to create and deploy Machine Learning models in a production environment at scale. Thus, using this platform, a data scientist can access powerful tools to solve problems such as classification, regression, clustering, etc. Sagemaker enables companies to accelerate the development process of their models by reducing costs and improving operational efficiency through its numerous features.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-aca3646 e-flex e-con-boxed e-con\" data-id=\"aca3646\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4625ad7 elementor-widget elementor-widget-heading\" data-id=\"4625ad7\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">The main functions of SageMaker<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4c1be0e elementor-widget elementor-widget-text-editor\" data-id=\"4c1be0e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">First of all, to use the SageMaker platform, the user has two options:<\/span><\/p><ul><li><span style=\"font-weight: 400\">Use a notebook instance through the Amazon SageMaker Notebooks Instances environment to run Jupyter notebooks. This environment includes common machine learning libraries and frameworks.<\/span><\/li><li><b>SageMaker Studio<\/b><span> is an integrated, cloud-based machine learning development environment that allows a user to manage the entire model development process through a user interface with numerous tools and advanced features.<\/span><\/li><li>For example, Amazon SageMaker Studio consists of, among other things, integrated notebooks, but also tools for managing data and models (training, deployment, and monitoring).<\/li><li><b>Amazon SageMaker Studio<\/b><span> thus offers a more comprehensive experience for machine learning development than the SageMaker Notebook environment. The choice is therefore up to the user and should be tailored to the project they want to implement.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6ffdfc3 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"6ffdfc3\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"274\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/image1-6-1024x351.png\" class=\"attachment-large size-large wp-image-169038\" alt=\"This illustration shows the process of Amazon SageMaker Notebook Instances\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/image1-6-1024x351.png 1024w, https:\/\/datascientest.com\/en\/files\/2023\/06\/image1-6-300x103.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/06\/image1-6-768x263.png 768w, https:\/\/datascientest.com\/en\/files\/2023\/06\/image1-6.png 1340w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4550c94 elementor-widget elementor-widget-text-editor\" data-id=\"4550c94\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Once the project is created, a variety of different functions are available to the user with the Sagemaker tool to develop their machine learning project. These functions include, for example :<\/span><\/p><ul><li style=\"font-weight: 400\"><span style=\"font-weight: 400\">The <\/span><b>preparation of the data.<\/b><\/li><li style=\"font-weight: 400\"><span style=\"font-weight: 400\">The <\/span><b>training of the models<\/b><span style=\"font-weight: 400\"> using <a href=\"https:\/\/datascientest.com\/en\/machine-learning-what-is-it-and-why-does-it-change-the-world\" target=\"_blank\" rel=\"noopener\">machine learning algorithms<\/a> preconstructed by AWS Sagemaker or by using common libraries such as Pytorch, TensorFlow and Scikit Learn.<\/span><\/li><li style=\"font-weight: 400\"><b>Deploying the models.<\/b><\/li><li style=\"font-weight: 400\"><b>Monitoring the models<\/b><span style=\"font-weight: 400\"> by examining the performance of a model in real time through performance analysis and anomaly detection.<\/span><\/li><li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Resource management<\/strong>.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6953a37 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"6953a37\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"257\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/image6-1-1024x329.png\" class=\"attachment-large size-large wp-image-169039\" alt=\"This illustration shows the process of the Amazon SageMaker Studio Notebooks\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/image6-1-1024x329.png 1024w, https:\/\/datascientest.com\/en\/files\/2023\/06\/image6-1-300x96.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/06\/image6-1-768x247.png 768w, https:\/\/datascientest.com\/en\/files\/2023\/06\/image6-1.png 1472w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-736e37f elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"736e37f\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/data-courses\" target=\"_blank\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Get to know our trainings<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-5d13e99 e-flex e-con-boxed e-con\" data-id=\"5d13e99\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-91b7dda elementor-widget elementor-widget-heading\" data-id=\"91b7dda\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How do I create and deploy a machine learning model using Amazon SageMaker?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-45cf766 elementor-widget elementor-widget-heading\" data-id=\"45cf766\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">1. Preparation of the data<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-883244f elementor-widget elementor-widget-text-editor\" data-id=\"883244f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">The first step is to prepare the data, which can be optimized using various tools from the platform:<\/span><\/p><ul><li><b>Sagemaker Clarify:<\/b><span style=\"font-weight: 400\"> This tool is able to detect biases in the training data to ensure that the model is accurate enough. It also suggests strategies to correct these biases.<\/span><\/li><li><b>SageMaker Ground Truth:<\/b><span> This feature is used to annotate datasets needed for training machine learning models. This reduces the cost of data labeling.<\/span><\/li><li><b>SageMaker Data Wrangler:<\/b><span> This feature cleans, normalizes, and transforms raw data into a dataset that can be used to <\/span><b>train a model<\/b><span>. The user is also provided with a graphical user interface to facilitate data preparation.<\/span><\/li><li><b>Geospatial ML:<\/b><span> This tool enables large-scale geospatial data analysis and processing.<\/span><\/li><li><b>Feature Store:<\/b><span> With the help of this service, the user can store, share and manage the features used in his models.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-549ff8e elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"549ff8e\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"268\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/image4-3-1024x343.png\" class=\"attachment-large size-large wp-image-169043\" alt=\"This illustration shows how to detect bias and explain model predictions.\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/image4-3-1024x343.png 1024w, https:\/\/datascientest.com\/en\/files\/2023\/06\/image4-3-300x101.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/06\/image4-3-768x257.png 768w, https:\/\/datascientest.com\/en\/files\/2023\/06\/image4-3.png 1370w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-2104880 e-flex e-con-boxed e-con\" data-id=\"2104880\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-247aded elementor-widget elementor-widget-heading\" data-id=\"247aded\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">2. Train model<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6771294 elementor-widget elementor-widget-text-editor\" data-id=\"6771294\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Regarding the training of a model, thanks to the platform, the user has several tools at his disposal, such as:<\/span><\/p><ul><li><b>SageMaker Autopilot:<\/b><span style=\"font-weight: 400\"> This tool enables automated machine learning development. It allows an inexperienced user to easily create a machine learning model. After you provide Amazon SageMaker Autopilot with all the data, the tool automatically creates and optimizes a model. With the help of this tool, the user can also figure out how the model makes decisions.<\/span><\/li><li><b>SageMaker Debugger:<\/b><span> With this feature, the user can debug models to get information about calculation errors and convergence problems. This is very useful to find out if the model fits the data well.<\/span><\/li><li><b>SageMaker Experiments:<\/b><span> this feature allows the developer to examine models, compare them and observe the logging of metrics.<\/span><\/li><li><strong>Automatic Model Tuning:<\/strong> This tool is able to automate the selection of optimal hyperparameters for a model through various search techniques.<\/li><li><b>Distributed Training:<\/b><span> this feature is useful for greatly reducing the time required to train models by parallelizing the training processes across multiple instances.<\/span><\/li><li><b>SageMaker Canvas:<\/b><span> this is a visual environment used to create, manage and visualize machine learning workflows. A workflow is a tool used to organize and manage the development steps of a model. It is also possible to share one&#8217;s workflows with other users to collaborate on projects.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-44a9089 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"44a9089\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/data-courses\" target=\"_blank\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Get to know the DataScientest courses<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c39314a elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"c39314a\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"376\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/image2-5.png\" class=\"attachment-large size-large wp-image-169047\" alt=\"This illustration shows how to easily manage and analyze Machine Learning experiments.\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/image2-5.png 871w, https:\/\/datascientest.com\/en\/files\/2023\/06\/image2-5-300x141.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/06\/image2-5-768x361.png 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-cd7bc7c e-flex e-con-boxed e-con\" data-id=\"cd7bc7c\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d69d4c4 elementor-widget elementor-widget-heading\" data-id=\"d69d4c4\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">3. Roll out the models<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f533b15 elementor-widget elementor-widget-text-editor\" data-id=\"f533b15\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">To roll out its model, the main feature that the user can use is Amazon SageMaker Edge. This feature can deploy machine learning models to edge devices and applications through a software development kit (SDK) that simplifies the integration of the deployed models.<\/span><\/p><p><span style=\"font-weight: 400\">Edge devices are hardware devices consisting of sensors, processors, and operating software, among others, that are used to collect, process, and store data in the field. Examples of edge devices include security cameras, connected watches, self-driving cars, and smart thermostats&#8230;). Edge applications, on the other hand, are software applications designed for use on edge devices. They can be used to process data, optimize performance, make decisions, and much more.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-bd7cc25 e-flex e-con-boxed e-con\" data-id=\"bd7cc25\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-06b640d elementor-widget elementor-widget-heading\" data-id=\"06b640d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">4. Maintenance of the rolled out model<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c60aaef elementor-widget elementor-widget-text-editor\" data-id=\"c60aaef\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">After deployment, users can apply the following functions to their deployed models:<\/span><\/p><ul><li><b>SageMaker Model Monitor:<\/b><span style=\"font-weight: 400\"> this feature monitors the performance of machine learning models that have been deployed in production. The tool can detect prediction errors, performance degradation and irregularities in data distribution. The final benefit of this tool is that it sends alerts to the user so they can take corrective action.<\/span><\/li><li><b>SageMaker Pipelines:<\/b><span> this service is used to create and manage machine learning workflows. It provides the user with an environment to create, validate and deploy the workflows using Docker containers.<\/span><\/li><li><b>SageMaker Test Shadow:<\/b><span> This feature can simulate predictions of machine learning models in a production environment with specific test data to validate the behavior of the models.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-be131ab elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"be131ab\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"313\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/image3-4-1-1024x400.png\" class=\"attachment-large size-large wp-image-169048\" alt=\"This illustration shows how to test model performance in production without impacting end users.\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/image3-4-1-1024x400.png 1024w, https:\/\/datascientest.com\/en\/files\/2023\/06\/image3-4-1-300x117.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/06\/image3-4-1-768x300.png 768w, https:\/\/datascientest.com\/en\/files\/2023\/06\/image3-4-1.png 1289w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-edf8b75 e-flex e-con-boxed e-con\" data-id=\"edf8b75\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c184fa5 elementor-widget elementor-widget-heading\" data-id=\"c184fa5\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Conclusion<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-758d304 elementor-widget elementor-widget-text-editor\" data-id=\"758d304\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\"><strong>Amazon SageMaker<\/strong> is a particularly useful and comprehensive platform for Data Scientists because it has a variety of tools that are high quality and interesting.<\/span><\/p><p><span style=\"font-weight: 400\">These tools can be used for all phases of developing a machine learning model, from preparing the data, to training and deploying the model, to maintaining the deployed model. So, the <strong>AWS SageMaker platform<\/strong> will be used more often by Data Scientists for Machine Learning because of its many centralized features.<\/span><\/p><p><span style=\"font-weight: 400\">If you want to further your education in Data Science to go deeper into the topics around Machine Learning, you should take a look at our <a href=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-course\" target=\"_blank\" rel=\"noopener\">courses in Machine Learning Engineering<\/a> and Data Science, which can also enable you to get the <strong>AWS certification<\/strong>.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bd9e43b elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"bd9e43b\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/aws-training\" target=\"_blank\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover the AWS training<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Amazon SageMaker is a cloud platform developed by Amazon Web Services (AWS) that is particularly useful for data science. Below you can learn more about the main features and how to use the tools of the platform from the development of a machine learning model to its deployment. Introduction to Amazon SageMaker Amazon SageMaker is [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":169033,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2412],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>AWS SageMaker: A guide for using the platform I DataScientest<\/title>\n<meta name=\"description\" content=\"AWS SageMaker is a cloud platform that is particularly useful for data science. 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