Checkout the Latest Courses and Hands-On Labs from Skill Me UP!
Keep your skills sharp and check out the Live Schedule too.
In this lab, you will use the Azure PowerShell cmdlets to manage common tasks with Azure Blob Storage.You will learn how to create an Azure Storage Account, create and manage containers, upload and download blobs, and manage leases and snapshots.
In this lab, you will create a virtual network, network security groups and an application security group. From there you will associate several security rules and then create several virtual machines associated with them to test filtering network traffic.
This course will discusses the implementation of cloud security in Microsoft 365, in addition to compliance and privacy concerns. You will learn about various identity and security methods as well as discussing the use of Azure Active Directory and AD DS. It will also discuss mobile device and application management and the use of Windows Intune.
This course will discuss the various licensing and subscription options available in Microsoft 365. It will also discuss the pricing and models for other cloud services and a comparison between cloud services versus on-premise. Finally, it will look at the process for creating a support request.
Azure Container Instances enables deployment of Docker containers onto Azure infrastructure without provisioning any virtual machines or adopting any higher-level service. In this tutorial, you build a small web application in Node.js and package it in a container that can be run using Azure Container Instances.
In this lab, you will provision how to provision a Databricks workspace, an Azure storage account, and a Spark cluster. You will then execute and manage a Spark Job.
In this lab, you will provision how to provision a Databricks workspace, an Azure storage account, and a Spark cluster. You will learn to use the Spark cluster to explore data using Spark Resilient Distributed Datasets (RDDs) and Spark Dataframes.
Spark structured streaming enables you to use the dataframe API to read and process an unbounded stream of data. This kind of processing is used in real-time scenarios to aggregate data over temporal intervals or windows. You can use Spark to process streaming data from a wide range of sources, including Azure Event Hubs, Kafka, and others. In this lab, you will run a Spark job to continually process a real-time stream of data.
Spark includes an API named Spark MLLib (often referred to as Spark ML), which you can use to create machine learning solutions. Machine learning is a technique in which you train a predictive model using a large volume of data so that when new data is submitted to the model it can predict unknown values. The most common types of machine learning are supervised learning and unsupervised learning. In a supervised learning scenario, you start with a large volume of data that includes both features (categorical and numeric values that describe characteristics of the entity you’re trying to predict something about) and labels (the value your model will predict. Training the model involves applying a statistical algorithm that fits the features to the labels. Because your initial data includes known values for the labels, you can train the model and test its accuracy with these known label values – giving you confidence that the model will work accurately with new data for which the label values aren’t known. Unsupervised learning is a technique in which there are no known label values, and the model is trained to group (or cluster) similar entities together based on their features.In this lab, we’ll focus on supervised learning; and specifically a type of machine learning called classification in which you train a model to identify which category, or class an entity belongs to. You will train a classifier to use features of flights that are enroute to an airport, and predict whether they will be late or on-time.
This is the first course of the AI-100 Exam Preparation Learning Path. In this course, we will discuss all the different APIs available and appropriate use cases for each API,we will discuss the various tools, technologies, and processes available to secure your AI data, andwe will discuss the various analytics solutions, storage solutions, and other services available to create an end to end solution.
In this hands-on lab, you will learn how Trey Research can leverage Deep Learning technologies to scan through their vehicle specification documents to find compliance issues with new regulations. You will standardize the model format to ONNX and observe how this simplifies inference runtime code, enabling pluggability of different models and targeting a broad range of runtime environments and most importantly, improves inferencing speed over the native model. You will build a DevOps pipeline to coordinate retrieving the latest best model from the model registry, packaging the web application, deploying the web application and inferencing web service. After a first successful deployment, you will make updates to both the model, the and web application, and execute the pipeline once to achieve an updated deployment. You will also learn how to monitor the model’s performance after it is deployed so Trey Research can be proactive with performance issues.At the end of this hands-on lab, you will be better able to implement end-to-end solutions that fully operationalize deep learning models, inclusive of all application components that depend on the model.
In this course, you will learn how to get started using the Azure PowerShell cmdlets. This will cover how to use the Azure Cloud Shell or install the Azure PowerShell cmdlets on your local computer. You will also learn how to navigate Azure Resources using the Cloud Shell and the Azure Cmdlets. Finally, the course will explore the various ways you can connect to Microsoft Azure; including logging in interactively, using a service principal or a managed identity.
In this lab, you will learn to use service principals with Azure PowerShell by first creating a service principal, assigning permissions to that principal, and finally logging into Azure with the service principal.
In this lab, you will learn to use a Manged Identity with Azure PowerShell by creating a virtual machine with a system managed identity. You will then assign the identity to a resource group using role-based access control (RBAC).
In this lab, you will train a classification model using Python in an Azure Machine Learning Notebook VM. The model will predict what type of bicycle a customer is most likely to buy. Some exploratory data analysis and feature engineering will be required.
In this lab, you want to see if there are models that perform better than the one you might manually create. You decide to use Azure Machine Learning service’s AutoML and HyperDrive to simultaneously execute a number of different types of classification models, compare the results, and recommend the best performing model. This will save you a lot of time picking the best model so you can get the solution delivered sooner.
In this lab, you will train the model you developed in the last lab on Azure using the Azure Machine Learning service and its Python SDK. After it has been trained, you will register the model to the registry and perform the steps necessary to deploy your model to Azure Machine Learning service where it can be leveraged by your company’s applications.
In this lab, you will use the Azure Migrate service to migrate the SmartHotel app which is currently hosted on an on-premises infrastructure hosted in Hyper-V to Azure Virtual Machines. During the lab, you will migrate this entire application stack to Azure using the Azure Migrate service. Note: this lab takes 60-75 minutes to fully deploy.
In this hands-on lab, you will implement a solution which combines both pre-built artificial intelligence (AI) in the form of various Cognitive Services, with custom AI in the form of services built and deployed with Azure Machine Learning service. You will learn to create intelligent solutions atop unstructured text data by designing and implementing a text analytics pipeline. You will discover how to build a binary classifier using a simple neural network that can be used to classify the textual data, as well as how to deploy multiple kinds of predictive services using Azure Machine Learning and learn to integrate with the Computer Vision API and the Text Analytics API from Cognitive Services.
In this lab, you will practice introductory concepts in managing Azure with PowerShell. You will learn to manage subscriptions, create resources, view resources, tag resources, and view resource tags. You will finally learn to delete resources.
In this module, you will gain the knowledge and skills to implement dependency management. Students will learn how to design a dependency management strategy and manage security and compliance.
In this module, you will gain the knowledge and skills to deploy an application infrastructure in DevOps pipelines. Students will learn how to implement infrastructure as code and configuration management, how to provision Azure infrastructure using common automation tools, and how to deploy an application infrastructure using various Azure services and deployment methodologies. Students will also learn how to integrate 3rd party deployment tools with Azure, such as Chef and Puppet to incorporate compliance and security into the release pipeline.
In this module, you will gain the knowledge and skills to implement continuous feedback. Students will learn how to recommend and design system feedback mechanisms, implement a process for routing system feedback to development teams, and optimize feedback mechanisms.
In this module, you will gain the knowledge and skills to design a DevOps strategy. Students will learn how to plan for transformation, select a project, and create team structures. Students will also learn how to develop quality and security strategies. Planning for migrating and consolidating artifacts and source control will also be covered.
In this lab, you will use Azure's Update Management solution to manage patches and updates for virtual machines. This involves enabling update management for a VM, assessing the status of updates, configuring alerts, scheduling update deployments, and finally viewing the results of update deployments.
This course provides an overview of cloud concepts and platforms. It also discusses benefits and processes for migrating to the cloud. It will also look at different Microsoft Cloud Services and Microsoft 365 service offerings.
This course will look at the fundamentals of Microsoft 365 including Office 365, Windows 10 and Enterprise Mobility + Security. It will also show a comparison between on-premise solutions and cloud based solutions.
In this lab you will learn to navigate the Azure Machine Learning Workspace and created an experiment using Automated Machine Learning. You will deploy an AML Workspace to a resource group, create compute, create an automated machine learning experiment and evaluate the results. Once complete you will deploy it to a container instance and prepare if for deployment.
This course is an introduction to Python. In this course you will learn which IDE is right for you, print statements, data types, control flow, Python functions and anonymous functions, methods, file io, and an introduction to Python packages.
In this module, you will gain the knowledge and skills to implement the DevOps practices of continuous integration. Students will learn how to implement continuous integration in an Azure DevOps pipeline, how to manage code quality and security principles, and how to implement a container build strategy.
In this module, you will gain the knowledge and skills to implement continuous delivery. Students will learn how to design a release strategy, set up a release management workflow, and implement an appropriate deployment pattern.
In this module, you will gain the knowledge and skills to implement DevOps processes. Students will learn how to use source control, scale Git for an enterprise, implement and manage build infrastructure, manage application configuration and secrets, and implement a mobile DevOps strategy.
This course provides an overview of what Chef configuration management is, and how you can use it to optimize your server automated configuration process. Starting from an introduction to Chef Software, you will learn about the Chef architecture, how to author recipes and cookbooks, and applying those to your systems.The goal of this course is to share a lot of hands-on experience, allowing you to follow along with the live demos. After going through this course, you will have a good understanding of Chef capabilities, have learned how to deploy your Chef Server and configure your Chef Workstation. You will also have authored 2 cookbook recipes to deploy a Linux Apache Web Server and a Windows Web Server, and validated the configuration updates.
In this hands-on lab, you will use Visual Studio 2019 to create a new web application that uses ASP.NET Core. You will learn the basics of the razor syntax, as well as how to add models and views.
In this hands-on lab, you will use Visual Studio Code to create a new web application that uses ASP.NET Core. You will learn the basics of the razor syntax, as well as how to add models, controllers, and views.
This overview has been developed and targeted specifically towards system administratorsand software developers on a Linux platform, who want to automate the deployment of anapplication (installation, upgrades, configuration files) and/or provision or configurean entire system.
In this lab, you will see how open source tools, such as Terraform, can be leveraged to implement Infrastructure as Code (IaC) and how to automate your infrastructure deployments in the cloud with Terraform and Azure Pipelines.
Azure DevOps supports two types of version control, Git and Team Foundation Version Control (TFVC). Here is a quick overview of the two vesion control systems:Team Foundation Verson Control (TFVC): TFVC is a centralized version control system. Typically, team memvers have only one version of each file on their dev machines. Historical data is maintained only on the server. Branches are path-based and created on the server.Git: Git is a distributed version control system. Git repositories can live locally (such as on a developer's machine). Each developer has a copy of the source repository on their dev machine. Developers can commit each set of changes on their dev machine and perform version control operations such as a history and compare without a network connection.Git is the default version control provider for new projects. You should use Git for version control in your projects unless you have a specific need for centralized version control features in TFVC.In this lab, you will learn how to establish a local Git repository, which can easily be syncronized with a centralized Git repository in Azure DevOps. In addition, you will learn about Git branching and merging support. You will use Visual Studio Code, but the same processess apply for using any Git-compatible client with Azure DevOps.
Application Insights is an extensible Application Performance Management (APM) service for web developers on multiple platforms. You can use it to monitor your live web applications and other services. It automatically detects performance anomalies, includes powerful analytics tools to help you diagnose issues, and helps you continuously improve performance and usability. It works for apps on a wide variety of platforms including .NET, Node.js and Java EE, hosted on-premises, hybrid, or any public cloud. It even integrates with your DevOps process with connection points available in a variety of development tools. It can even monitor and analyze telemetry from mobile apps by integrating with Visual Studio App Center.In this lab, you'll learn about how you can add Application Insights to an existing web application, as well as how to monitor the application via the Azure portal.
In this hands-on lab you will work with the fundamentals of PowerShell. Topics will include checking the PowerShell Version, getting help on commands, managing your profile, and the basics of using PowerShell variables, piping and Cmdlets.
In this hands-on lab, you will learn how to format output using PowerShell. This will involve formatting with the pipeline and format-table/format list. You will also learn to use the --f operator and format with hash tables. Finally, you will use out-of-grid view.
In this hands-on lab you will work with the PowerShell Integrated Scripting Environment (ISE). This lab will begin by demonstrating the general layout and structure of the ISE. Then you will learn about using Intellisense. Finally you will learn about using Snippets in the PowerShell ISE.
In this hands-on lab, you will learn about using modules to organize your code with PowerShell. You will examine snap-ins and modules, create a simple module, and create a module manifest.
In this hands-on lab, you will learn how to use objects with PowerShell. You will work with .NET objects, COM objects, and WMI objects.
In this hands-on lab, you will learn about using PowerShell Providers. This lab will involve viewing providers, using the registry provider, and using the certificate provider.