{"id":422,"date":"2021-11-03T12:48:15","date_gmt":"2021-11-03T12:48:15","guid":{"rendered":"http:\/\/18.141.20.153\/?p=422"},"modified":"2025-08-21T10:01:38","modified_gmt":"2025-08-21T10:01:38","slug":"how-to-build-machine-learning-models-quickly-using-amazon-sagemaker","status":"publish","type":"post","link":"https:\/\/learning.workfall.com\/learning\/blog\/how-to-build-machine-learning-models-quickly-using-amazon-sagemaker\/","title":{"rendered":"How to build Machine Learning Models quickly using Amazon Sagemaker?"},"content":{"rendered":"<span class=\"rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">Reading Time: <\/span> <span class=\"rt-time\">5<\/span> <span class=\"rt-label rt-postfix\">minutes<\/span><\/span>\n<p><img src=\"https:\/\/lh4.googleusercontent.com\/oIwTSR_txJiGBCbRVGVj2Q3BtPNMZ__kENVqt-wFW674Klov82OGsKV7UGx_V6eOWJovLb-SLPcVcD3p5RwIDuXT3N7e8E-Q7BtQReGwa1YCqsTUKi8plp-jfhPZxSzEgNVBjS3oGYXziH6rOhDrzqHim2WQIVktue1J9HdIbY5JtsaSLWIjSGGWxyZ-\" style=\"width: 1600px;\"><\/p>\n\n\n\n<p class=\"has-text-align-justify\">Amazon Sagemaker enables data scientists and developers to train and deploy machine learning models for performing the analysis of the dataset.&nbsp;<\/p>\n\n\n\n<p class=\"has-text-align-justify\"><a href=\"https:\/\/www.workfall.com\/learning\/blog\/how-to-build-ml-models-to-generate-accurate-predictions-without-writing-code-using-amazon-sagemaker-canvas\/\">Amazon Sagemaker<\/a> is a cloud-based machine learning tool that is used to build, train, test, and deploy machine learning models. Amazon launched this fully managed machine learning service in 2017. During its initial launch, it was limited to some regions but now it has flourished across all the regions of AWS.<\/p>\n\n\n\n<p>Amazon Sagemaker has several key features:<\/p>\n\n\n\n<ul><li>It provides several built-in ML algorithms to train your datasets.<\/li><li>Sagemaker also provides pre-trained models that can be deployed as-is<\/li><li>It automatically scales model inference to multiple server instances.<\/li><li>Sagemaker also offers managed instances of Tensorflow and Apache MXnet. So, that the developers can create their own ML algorithms from scratch.<\/li><\/ul>\n\n\n\n<p class=\"has-text-align-justify\">Machine learning is an emerging technology and it would be icing on the cake if you already know how to build, train and deploy machine learning models using Sagemaker.<\/p>\n\n\n\n<p class=\"has-text-align-justify\">So, we are here to guide you with the step-by-step hands-on exercise on How to build machine learning models using <a href=\"https:\/\/aws.amazon.com\/sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">Amazon Sagemaker<\/a>.<\/p>\n\n\n\n<p>Select Amazon Sagemaker from services. Navigate to Dashboard. Here we will have to create a Notebook Instance.<\/p>\n\n\n\n<p>So, let\u2019s create Notebook first. Click on Notebook instances.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh4.googleusercontent.com\/rHE10KNPaHw1QxR8XRTIIDTK1BHan0Tnme5uPRrRgeKYMwl5lOHvQg-8f_kHMIm7zYvcwZQXRzBmQJoiFJgA9j9hitfEy0RE_LrgWwlZIUuxFFFnGAba8YawYBnT7dZuHI_hoiRClzqXcj58_kUR\" alt=\"How to build Machine Learning Models quickly using Amazon Sagemaker?\"\/><\/figure>\n\n\n\n<p>You will be redirected to the Notebook Instances page. Click on create notebook instance.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh6.googleusercontent.com\/jmZt-iHcYrnVv6b7ewWrRcodij2sdHBNDsbZX8Kvmtm6Dv1GTG8uyx8E10PQkZUbQUZDiUnYUXBKYgIVlRmuftOnBnjI00do-BZe6nOF5vpbNoQ_NnHR3TzkJEALSZco0pzUq5gHvzRnMLYxCsgN\" alt=\"\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\">Here, we will provide a name for our notebook instance. For this hands-on exercise, we are choosing the default Notebook Instance Type i.e. ml.t2.medium.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh6.googleusercontent.com\/rigcPQje1eu2i3jVU5pKdXlOuCIzcuqV1Wta1DY-Vmayd99DoyaD6plaWIy-NnNSaBBXUs_vTEGnY8f_qeg8rIfBskN3AXIEhHuzDHHwpwg2XIlPcY5OXmKpBQYPvimtwEapewUZWu2Lej_AVIQX\" alt=\"\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\">In Permissions and encryption, we will provide the IAM role. Here, you can create a new IAM role. You can also attach an existing role here.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh5.googleusercontent.com\/nqj0XJxl3hECz8Iu_PAJ5XzI1syPjvNVFbHONM_7bNx1IpNYP5ww7iGQutvEi_dFS8b_AzejAFFsNwWzVxY-cvrt4ZknOgKjx3H0JCCXw8BBGv83qfRcYO4MqE5XRVC_gzO2-a-TPWFq_H-Urm63\" alt=\"How to build Machine Learning Models quickly using Amazon Sagemaker?\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\">For creating a new role, click on Create a new role from the dropdown. This IAM role is required by Notebook Instances to access other services like Sagemaker, S3, etc.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh5.googleusercontent.com\/Pp_Ai9fQQmxF6P6NZUQlVT_PJwUe3dZyWP3FiA6pYvUk2XA4QBJi6Q-SzFzgIwpxxh6s56apeDAOQJ0aceA2TK6ftjzG31T1INU9ej3YcN0kxbNmQO4znNc0QGIJdPAzvoLUGwYpWulWDKQeP5DQ\" alt=\"\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\">Here, you can select Any S3 bucket or you can give the name of any specific S3 bucket that you want. This will allow the sagemaker to access your buckets and their contents.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh3.googleusercontent.com\/W00uaq3V6Z8llP3H03BWHgitUK_FLEHwNsmrTVhE5aPPkiMSoENhqclHCIW_rS_pNrOpwTkAeBl32n3C14nSSedPohD-uYbr3FyhsKHt4wNoG1rVfOHwVeh6md8Os1xJncM6ZwLfcTiopcHwdMJW\" alt=\"How to build Machine Learning Models quickly using Amazon Sagemaker?\"\/><\/figure>\n\n\n\n<p>We are using the existing role SagemakerRole to perform this example. Now Click on Create Notebook Instance.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh6.googleusercontent.com\/zxbk9e8WCBtRp4aVhYEWcE1OU_Mt4PDcMLdP9IWIzZBexCBIbl0127EBSXONJa7yndnWPHMqUPpCR7lHsRhfaGcH-7n3KhUJYFbzZnIpHjtXYUT0dBDvMLsn3GdsVu2HdU73IUYu_9mCaFBWgYtA\" alt=\"\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\">Our Notebook instance will be in pending state. It will take a minimum of 2-3 mins for your Notebook instance to come into InService state.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh5.googleusercontent.com\/kkQ48swV6xbiy3ep-Zw4CdmeTr8dm1AdS06w6elbcoK2eFwTAqMR4btIfeku8YY1huKgu0u0f97ajNF8OAUPtwMx0XYm6TIxdAhs9vrUmSPbHxSFv1WC3fSeZInCCQow5_YZwstFNgC2rst_Att7\" alt=\"\"\/><\/figure>\n\n\n\n<p>After it appears in <strong>InService<\/strong> state, you can go to Actions and then select <strong>Open Jupyter<\/strong> from drop down.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh5.googleusercontent.com\/bTKeJ1OGCrihmpgJivgIaZxGKcoyOiIgZejSN9JMgHmm3IPunyRyUMyWDghyb76YRcvUk5mmH4q8uc7ZYzJij7mZKN368f8HUn3rm88zW1-G3gt3JFDsXZaaOflZ1_E8vAflUiQGXUwRz2aDwAPS\" alt=\"How to build Machine Learning Models quickly using Amazon Sagemaker?\"\/><\/figure>\n\n\n\n<p>Once you are into your notebook instance, go to Files then New and select conda_python3 from drop down.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh6.googleusercontent.com\/-Pk7pXCixEcHF3RafkPD9VicHbls1cKbvXx5O7P-xD8x5ZapaTnrvITKL73-Hs0gCDNmti0wdm0Q97CyF39ulad8eQepYoG3MYcp7nkZYalXE0L0KPOZhAdVJQ0H-s92RqffMLlxsziy7d09e36R\" alt=\"\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\">Jupyter Notebook will open in a new window. Here we will write the ML algorithms to build our model from the data set.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh3.googleusercontent.com\/-pV7EMDHN2Op5T3c3mmWvHUKWDfIbQjMHdzZ-Nih8JmxFiUW8xfuhZQmbfgRe7sVMxhuqdG1Vnkr4xpBzHnztaWFHX1Cufw5OEk39zUK8lkSLQlY7_sqsok1JUK8PQ3HnANMvfoGtsg_J7E3Dqdc\" alt=\"How to build Machine Learning Models quickly using Amazon Sagemaker?\"\/><\/figure>\n\n\n\n<p>Here, we will import all the important python libraries that we will need for this exercise.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh4.googleusercontent.com\/6Nk7TEme5Y9001xfUdG_iodKYWWyMgqj5vA4VSnElS-egY31K82LY5g7V0xfvos4B8d4oU427BNV31mkig-BtGClPHVKTiSGYWB-F39PzdLp1aB5MMxN4ys-SSLwhFHhKhvz4EgVjBlvKmLnh6Tf\" alt=\"\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\">Next, we will provide the bucket name where the model and train data will be stored. The prefix will be created inside the bucket.<\/p>\n\n\n\n<p>Also, we will define the IAM role of the sagemaker.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh6.googleusercontent.com\/wK7kMSGO_poBx30EE7aeWYthNStkKCqTuaEO5V9NCdnvVVsT17PzaoRvxs6bHYMOIHjFQYE4scYMjAJzcNDN_iMY4XPBz43XBF8VICQ4wGnR2fepJEoDfhWUeOI4ab8sJkug7nbivOTsj4c9po9q\" alt=\"How to build Machine Learning Models quickly using Amazon Sagemaker?\"\/><\/figure>\n\n\n\n<p>In this step, we will fetch the dataset.csv file from the bucket. Sagemaker will read the file from S3 location.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh3.googleusercontent.com\/Tz0lwGQA5NoVmWSiRRe3QOXrSHobYows40DEDH05TTTh5rlw_ieawTbVBZW1ZYGIBbvH6_ui27QWgcbWqrSJH3CbmgQkodCRFh5HVrqgUX2kw4lfkCcGZbpjsdTXlecGI4tojFZY7UcIH6FzDNE-\" alt=\"How to build Machine Learning Models quickly using Amazon Sagemaker?\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\">This will be the output of the file. All the data of the dataset.csv file will be printed. This output will also show you the rows and columns present in the file.<\/p>\n\n\n\n<p>In this file, there are 13932 rows and 7 columns.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh4.googleusercontent.com\/eeEl99xqRDzDb1k2N6j-mnQCLJ-3Dcoj4rdEPPe1RsnjHKFlqov5T7gpLmtMO4l2F4Gh1Ft1JcxkQpvIyodlHse9jj3pu3AJ64So_YEnxjVVLWhBI4R2lG_K04ZwUbwvepVC7tMRyV6iwXGvFU61\" alt=\"\"\/><\/figure>\n\n\n\n<p>Now, this file will get saved in our dataframe. Sagemaker will be able to read it from there using the below algorithm.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh3.googleusercontent.com\/JNYAq0IK1JUt8r3OeW0z1FwiP1PVLLfTrOuM8Y5_K3QiI1E8sK4O_zn_H3XGkev7opGcdZxDQbBe8mBt6OATgcnQXSstON8SMGsHZXpe-9vC9t2PGitbHDSRTVs6gLXowSZXvpZlmMlcULGbQVsg\" alt=\"\"\/><\/figure>\n\n\n\n<p>The below-shown table will be the output.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh3.googleusercontent.com\/p2Yesf4fUye8dpXpDocoTGdKTNUvDaRCabyXPUAmOrXPoDmIoHJNRnQbXjYNUDxXsCCCH6aXcLz-lgCK54x-eMoSSSo8VC2Rh1-Isxg12nOiNAnRbXBIEd9NBnV9uwYDQY49xnoYBeYkxhu0gVvx\" alt=\"How to build Machine Learning Models quickly using Amazon Sagemaker?\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\">Now, we will apply some algorithms to refine this data. We will use matplotlib of python to create a histogram from the given data.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh6.googleusercontent.com\/5qIRsgfj4DVZZR61YJ24_SYr9s25lNIU0tWy_hUpw6VmQO2Je_yWtaUkQ953TLXetF9Dp7O92XeMCJVGN1IJU3C2Euv73zPP5csrkfMuZ1OGECQ9XhDdbQ6D8vQ5Sr9QMnwEsRIezX51tmhbH54f\" alt=\"How to build Machine Learning Models quickly using Amazon Sagemaker?\"\/><\/figure>\n\n\n\n<p>The output will show the individual column and its observation as shown below.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh6.googleusercontent.com\/vSVGEjfQLSdfns4HnbBw_6PffTYL7LVnEAvkZNcikpW5_KRNfA8N5xA8p8qs0kI7k30ReOWg72Xo07EL8l12WWD2HyA8aEIe8wcjlRCaUn1H8L35vvzn7RqycigGEZZSgGal24kZtFO9BDV8KBxH\" alt=\"\"\/><\/figure>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh5.googleusercontent.com\/crrL3YhvCpLck_xJTGOK25ICWL2jm6_xOhZb2o2xv2ITRURLHdoC2kzMAY5ZRnhku0sE0BsJVV0fVgWdD5ay-qi3rKaQ5ZM9XHInWvgL3B-uupCodmcPfVqwaVo184Wxx2bQ2l-Aa70gxRkSoeRI\" alt=\"How to build Machine Learning Models quickly using Amazon Sagemaker?\"\/><\/figure>\n\n\n\n<p>Here, we will drop the value column from the dataset.csv<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh5.googleusercontent.com\/eI04n9PRCuJX0cZxKQ_01msuACIuUh9qe8q42TV1OeZ2O91dOBycku59mSzQ12U7qG4ff7NNFdq-2P0vEKLow7Ohbz_3ZWFNgtQ758mAO_DMQq8uahlRplvm5relCDECwEDBxkB8MYWiAkH7PbtL\" alt=\"How to build Machine Learning Models quickly using Amazon Sagemaker?\"\/><\/figure>\n\n\n\n<p>Here, we will compare individual columns with the year column.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh5.googleusercontent.com\/9j-BrhrSqI3gObGKrYgxfvUOmCEpmiag69xH4UuXRqocdg6_oDDB3GPC7i0gFPrhqFlczg0Ye-Ft1gy6p2pvmgmWLthCI0apZZrWLHoSNtEtdlmnsJD1m3kOWHST85ZaxnP0RNKaxBahOVQ_Yf5K\" alt=\"How to build Machine Learning Models quickly using Amazon Sagemaker?\"\/><\/figure>\n\n\n\n<p>This will be the required output from the above algorithm.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh5.googleusercontent.com\/c2prsedK-5AP0si5zMsqHTvMiQNR6K8bcRtPxjNVgKHDqyMzDSbrkRcFOVpDnVvKZn5QtKU_HAmgV77qmxACacLqRmRFEtCXMcoRzN-vSHYfxfHU0NEXwqWSj-r0txTJs_8IhrVcNSBo5jGBCLgl\" alt=\"How to build Machine Learning Models quickly using Amazon Sagemaker?\"\/><\/figure>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh6.googleusercontent.com\/AtpT634OwVcmtvGiCt0z3XdFreHPZDry8tLRBeV5CzTnuc4nhwzemFT2_WzVC2yxbqjMi_4cSZb70dr1MXEKxH-Zum-dZyk8BzzSDFta1GxaGPi5wW7rCGwzwVePkyUeQNrpDpgNxAYkcga9wk7p\" alt=\"\"\/><\/figure>\n\n\n\n<p>Now, we will plot the data on the graph.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh4.googleusercontent.com\/HoUMuvixPnECXVD5l3qBbzAjS2uCr3b7pnCXZresyluZSdcwW5FimHrD4hltg5bfXGk5ATeTUdEvx-d7GZFDdJG8-kicMCYiyjQJ7vO7ewxN9V59nGD7FZ26Mhpp4DJkZurTyhG_DUgTnbfVLfzy\" alt=\"\"\/><\/figure>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh5.googleusercontent.com\/hcPHUeZpaDe1EO1ug58oSHjH6rt4qkxxonDVYyp-7qotOxRrLePcZoEaRec0zdD4FDFYuBSLWTkq51nyY5u5YPH-H_hg596MKj8ekJvyVzegrtenYVym4UDYEpx6OUunhd24jds3IgD-tjofewYa\" alt=\"How to build Machine Learning Models quickly using Amazon Sagemaker?\"\/><\/figure>\n\n\n\n<p>We will drop the below columns from the table. They are not required for the prediction.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh3.googleusercontent.com\/I_XDSUV5tx6aWN1AZgCluzO_TSOrpCQnyBZ4iV3yal4rmc45dNwlAlY3yx054e043oGlON3oRE_z78nlZPncv8Civ19iUa2BaOPZ0J6ORqNRakaeP4FUduhCzaThswNZhXIvVbGpEbL1NyU119Sm\" alt=\"\"\/><\/figure>\n\n\n\n<p>Now, we will split the data into two parts and store them into train.csv and validate.csv<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh5.googleusercontent.com\/K_GF93ULvZGgwsSR3LIubFVquLRCoxbLiVfmN5nqD8DW6jSkOo5jhWhYsp7MOGsYHEYc0xT2cqXAfuO4-fSma1cpGn5D4HotdpAdVcPKR2JRvu4UEBqeFqdzrauhKWyRAEb4j4PLR-54cMalyFlr\" alt=\"\"\/><\/figure>\n\n\n\n<p>In this step, we will upload the data into the S3 bucket using the below boto3 algorithm.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh3.googleusercontent.com\/z2x2e2HFO-c3QZR9XLO57fAZyvt-4bckLG-FeHiG448q5XdUxbJV8AgdPgEItt3vAsYW6mGFUjywhgU7Pq4zWymyagm3U_dsiSjWhVPnKUSpmfjSXypwV2x-ia0NJWnWxi4DNyrzOhUSBKnaUWMH\" alt=\"\"\/><\/figure>\n\n\n\n<p>The two folders will be created in the bucket as below and CSV files will be saved with the name train.csv and validate.csv<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh6.googleusercontent.com\/yLTzsioB9AS0FferEi3l2yIRyqfRY3Nf1SwSb38ARIMpoFBJQBXlRmqQgEDiaWeXHXsYqQEFR8QWlDvGutQAItqAfJJLH2VQUs8vvVw36sVTdZqVRF07B-Mxn-13AiK1jFV5f4yOm7_6T9876_Iz\" alt=\"\"\/><\/figure>\n\n\n\n<p>This is how we will analyze our data set using the machine learning algorithms in Amazon Sagemaker.<\/p>\n\n\n\n<p class=\"has-text-align-justify\">Using this, we can easily find out valuable insights from the data and we can also make future predictions accordingly using the above algorithms.&nbsp;<\/p>\n\n\n\n<p>Hope this information is helpful. We will keep sharing more about how to use new AWS services. Stay tuned!&nbsp;<\/p>\n\n\n\n<p>Meanwhile \u2026<\/p>\n\n\n\n<p><strong>Keep Exploring -&gt; Keep Learning -&gt; Keep Mastering<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p><span class=\"rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">Reading Time: <\/span> <span class=\"rt-time\">5<\/span> <span class=\"rt-label rt-postfix\">minutes<\/span><\/span> Amazon Sagemaker enables data scientists and developers to train and deploy machine learning models for performing the analysis of the dataset.&nbsp; Amazon Sagemaker is a cloud-based machine learning tool that is used to build, train, test, and deploy machine learning models. Amazon launched this fully managed machine learning service in 2017. During its initial launch, [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":423,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"spay_email":""},"categories":[2],"tags":[80,129,3,128,6],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v19.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How to build Machine Learning Models quickly using Amazon Sagemaker? - The Workfall Blog<\/title>\n<meta name=\"description\" content=\"Amazon Sagemaker is a cloud-based machine learning tool that is used to build, train, test, and deploy machine learning models.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/learning.workfall.com\/learning\/blog\/how-to-build-machine-learning-models-quickly-using-amazon-sagemaker\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to build Machine Learning Models quickly using Amazon Sagemaker? 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