{"id":170418,"date":"2023-09-10T13:57:34","date_gmt":"2023-09-10T12:57:34","guid":{"rendered":"https:\/\/liora.io\/en\/?p=170418"},"modified":"2026-02-06T08:58:55","modified_gmt":"2026-02-06T07:58:55","slug":"azure-course-master-azure-cloud-with-our-comprehensive-course","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/azure-course-master-azure-cloud-with-our-comprehensive-course","title":{"rendered":"Azure Course: Master Azure Cloud with Our Comprehensive Course"},"content":{"rendered":"<p><strong>Azure Course: Azure Machine Learning is a web service for creating and deploying machine learning (ML) models for data science teams. It enables the creation, testing, management, deployment, or monitoring of these models in a scalable cloud environment, allowing for big data analysis and predictive analytics.<\/strong><\/p>\t\t\n\t\t\t<h3>Objectives of a Microsoft Azure Course:<\/h3>\t\t\n\t\t<p>When it comes to creating, training, and deploying Machine Learning models, the tools typically used include:<\/p><p><strong>1. Azure Machine Learning Studio:<\/strong> This is a workspace where you create, build, and train machine learning models.<\/p><p><strong>2. Azure Machine Learning for Visual Studio Code Extension:<\/strong> It&#8217;s a free extension that allows you to manage resources, model training workflows, and deployments within Visual Studio Code.<\/p><p>During practical exercises, an <a href=\"https:\/\/liora.io\/en\/what-you-didnt-know-about-azure-databricks\">Azure Machine Learning training<\/a> aims to enable the student to:<\/p><ul><li>Get hands-on experience with the Azure Machine Learning service interface.<\/li><li>Choose the appropriate algorithm and variables for solving specific problems.<\/li><li>Master programming languages commonly used to optimize Azure Machine Learning (such as R and Python)<\/li><li>Gain practical experience with a dedicated web service for machine learning.<\/li><\/ul><p>By the end of the training, students should be proficient in using Azure Machine Learning to create, train, and deploy machine learning models effectively.<\/p>\t\t\n\t\t\t\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/\">Azure course<\/a><\/div><\/div>\n\n\t\t\t<h3>Azure Course: Prerequisites for training in Azure Machine Learning <\/h3>\t\t\n\t\t<p>Before starting an Azure Machine Learning training, it is important to have some fundamentals:<\/p><p>1. Understanding of Machine Learning concepts.<br>2. Knowledge of Cloud Computing concepts.<br>3. General understanding of containers and orchestration.<br>4. Programming experience in <a href=\"https:\/\/liora.io\/en\/machine-learning-python-where-to-start\">Python or R.<\/a><br>5. Experience working with a command line.<\/p>\t\t\n\t\t\t<h3>Main targets of Azure Machine Learning training<\/h3>\t\t\n\t\t<p>An<strong> Azure Course<\/strong> is designed for engineers who want to use Microsoft Azure&#8217;s &#8220;drag-and-drop&#8221; platform to deploy machine learning workloads without the need to purchase software and hardware or worry about maintenance and deployment. In other words, it is intended for:<\/p><ul><li><strong>Data Scientists<\/strong><\/li><li><a href=\"https:\/\/liora.io\/en\/average-data-engineer-salary-in-toronto\"><strong>Data Engineers<\/strong><\/a><\/li><li><strong>DevOps Engineers<\/strong> interested in deploying machine learning models interested in deploying machine learning models<\/li><li><strong>Software Engineers<\/strong> looking to automate the integration and deployment of machine learning capabilities within their applications<\/li><\/ul>\t\t\n\t\t\t<h3>Content of an Azure Machine Learning training course<\/h3>\t\t\n\t\t<p>An Azure Machine Learning course primarily focuses on getting hands-on experience with the workflow on this cloud service. Therefore, similar to running a workflow on the Azure Machine Learning service, it consists of three steps:<\/p>\t\t\n\t\t\t<h4>1. Preparing the data<\/h4>\t\t\n\t\t<p>This is the first step in creating a machine learning model, which involves collecting and processing data from a Datastore and Datasets.<\/p><p>Here are some examples of supported Azure storage services that can be registered as datastores:<\/p><ul><li>Azure Data Lake<\/li><li>Azure SQL Database<br>&#8211; Databricks File System<br>&#8211; Azure Blob Storage<\/li><\/ul>\t\t\n\t\t\t<h4>2. Experiments<\/h4>\t\t\n\t\t<p>Once the data is registered and stored in the <strong>dataset,<\/strong> the next step is to create, train, and test the model.<\/p><p>The model is a piece of code that takes inputs and produces outputs for those inputs. <strong>Developing an ML model<\/strong> involves selecting an algorithm, having data, and tuning hyperparameters.<\/p><p>Training involves an iterative process that provides a trained model inheriting what it has learned during the training process. The model is obtained by <strong>running it in Azure Machine Learning.<\/strong><\/p><p>Of course, you also need a compute resource where you run the training script. Azure Machine Learning has the advantage of allowing machine learning to be done on different compute targets:<\/p><ul><li>Local Compute: the compute context where the experiment submission code runs.<\/li><li>Compute Cluster: a virtual cluster managed by Azure Machine Learning.<\/li><li>Inference Cluster: a deployment target based on containers.<\/li><li>Attached Compute: Azure Databricks, Azure Data Analytics, etc.<\/li><\/ul><p>&nbsp;<\/p>\t\t\n\t\t\t<h4>3. Deployment<\/h4>\t\t\n\t\t<p>Once the model is trained and tested, it&#8217;s stored in the Model Registry and then deployed to a Web service or IoT modules. The registered model is deployed as a service endpoint. It <strong>instantiates the image<\/strong> into a web service that is then hosted in the cloud or within an IoT module for use in an embedded device deployment.<\/p><p>At the end of the training, students will be able to:<\/p><ul><li>Write highly accurate Machine Learning models using Python or R programming tools.<\/li><li>Leverage datasets and algorithms available on Azure ML to train and track Machine Learning and Deep Learning models.<\/li><li>Use the interactive Azure Machine Learning workspace to collaboratively develop machine learning models.<\/li><\/ul>\t\t\n\t\t\t\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/\">Discover our courses<\/a><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Azure Course: Azure Machine Learning is a web service for creating and deploying machine learning (ML) models for data science teams. It enables the creation, testing, management, deployment, or monitoring of these models in a scalable cloud environment, allowing for big data analysis and predictive analytics. Objectives of a Microsoft Azure Course: When it comes [&hellip;]<\/p>\n","protected":false},"author":80,"featured_media":170420,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"_acf_changed":false,"editor_notices":[],"footnotes":""},"categories":[2433],"class_list":["post-170418","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-ai"],"acf":[],"_links":{"self":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/170418","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/users\/80"}],"replies":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/comments?post=170418"}],"version-history":[{"count":1,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/170418\/revisions"}],"predecessor-version":[{"id":206365,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/170418\/revisions\/206365"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media\/170420"}],"wp:attachment":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media?parent=170418"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/categories?post=170418"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}