Skill Me Up Provides comprehensive Microsoft training for Devlopers focusing on Microsoft Azure Platform as a Service including, AI, Web Apps, Cosmos DB, SQL Database, Containers, DevOps and more. On-Demand Courses, Live Labs, and Live TrainingLive Course Schedule
In this learning path, you will learn key concepts about the cloud and various Microsoft Azure Services. From there, you will learn core concepts such as various PaaS and IaaS services including management tools. This course will also cover several key concepts for security and compliance, as well as a brief look at a Azure pricing and support. This course will help you prepare for AZ 900 Microsoft Azure Fundamentals.
This learning path contains a collection of courses and hands-on labs designed to help you pass the exam AZ-203 Developing Solutions for Microsoft Azure. More classes will be added shortly!
Students who complete the courses in this learning path will be able to analyze the requirements for AI solutions in Microsoft Azure, recommend the appropriate tools and technologies to implement AI solutions in Microsoft Azure, and implement those solutions in a manner that meet scalability and performance requirements. Students who complete each course in this learning path are on their way towards gaining the knowledge necessary to complete the AI-100 exam. (https://www.microsoft.com/en-us/learning/exam-ai-100.aspx).
In this learning path, you will learn how to design and implement web apps using PaaS services in Microsoft Azure. Technologies will include App Services, SQL Database, Storage, Redis, App Insights, and Cosmos DB to build a diverse set of web application types.
In this course, you will learn about Microsoft Azure Resource Manager which is the deployment and management service for resources in Azure. It is the consistent layer for creating, updating, and deleting resources in an Azure Subscription. This course will explain the architecture of resource manager and take a deep dive into topics such as resource providers and resources.
Welcome to the AI and Cognitive Services for Decision Makers course! In this course, we’ll start by discussing how AI can empower innovation. Then we’ll look at the steps of the Journey to AI for what is needed to implement Artificial Intelligence. Finally, we’ll take a look at the AI and Cognitive Services within Microsoft Azure and what they have to offer for implementing AI for your business.
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.
This course gives you an introduction to App Insights. This course begins by covering the importance of gathering telemetry data and how doing this correctly can help overcome usage and performance blindness usually associated to production workloads. Once we’ve covered the introductory aspects, I will start deep diving the monitoring aspects, where the plan is to show how telemetry data can be gathered for both web and non-web applications (module 2). Right after that, I will show the more advanced topics and services of App Insights and eventually finish the course with the 3rd party integration and security aspects of the service. This course is intended especially for Software Developers, no matter whether they are hosting their apps in Azure, AWS or their own private datacenter.
In this course, we’ll begin with an overview of Azure IoT Platforms. We'll then look deeper at the Raspberry Pi and Arduino hardware platforms for prototyping and building industrial IoT solutions. We’ll also look at the Microsoft Azure Sphere platform for IoT and the Windows 10 IoT operating system. In conclusion, we'll provide an overview of the Azure Certified for IoT program.
This course is an introduction to Microsoft Azure Machine Learning Services. In this course you will learn to navigate the AML Services interface, create notebook servers, create compute clusters, manage AML Services from a notebook, deploy models, and create an Automated Machine Learning experiment.
In this introductory course, students are walked through building an enterprise web solution using Microsoft Azure Platform as a Service (PaaS) components and Visual Studio 2017.
In this class on Xamarin Forms, you will learn how to create cross platform applications for iOS, Android and Windows UWP and share C# code for both the UI and the logic that drives it. We will cover the solution structure, layout system, basic controls and different app styles you can create. XAML is supported in Xamarin Forms and we will look at how to add pages, provide behavior and even change property values in a device-specific fashion. We will explore how the Xamarin Forms layout system works and show you how to best utilize the StackLayout and Grid layout containers, as well working with ListView and Image views. You will learn how to build an Azure Mobile App on the Azure Portal using the Quickstart for Xamarin Forms. The App Service that the Quickstart generates will be customized to add more tables, DTOs and Controllers. Finally, you will modify an existing app to become a Xamarin Forms Client for Azure with offline synchronization.
Logic Apps are a fully managed PaaS service that is part of the Azure App Service service. Logic Apps allows any technical user or developer the ability to automate business process execution and workflow via an easy-to-use visual designer or with Visual Studio. Students will obtain an understanding of the Logic App features and get practical experience with building Logic App workflows.
This course helps Azure developers learn how to develop an app service logic app, integrate Azure search within solutions, establish API gateways, develop event based solutions and develop message based solutions
This is the second course of the AI-100 Exam Preparation Learning Path.In this course, we will discuss how to analyze the different business scenarios and translate them into different tools available,we will design a solution using Cognitive Services, we will discuss various use cases related to bot services and LUIS and discuss how to design a solution using these technologies,we will discuss the various High Performance Computing solutions that rely on computer infrastructure within the cloud, on-premises, and within hybrid scenarios, andwe will discuss security, compliance, and governance as it pertains to designing an AI solution in the cloud.
In this course students will gain the knowledge and skills needed to implement Azure IaaS services and features in their development solutions. The course covers provisioning virtual machines, using Batch Service to deploy/maintain resources, and how to create containerized solutions by using Azure Kubernetes Service.
In this course you will gain the knowledge and skills needed to implement Azure Platform as a Service feature and services in their development solutions. Students will learn how to create and manage Azure App Service resources, integrate push and offline sync in their mobile apps, and how to document an API. Students will also learn how to create and test Azure Functions.
In this course students will gain the knowledge and skills needed to leverage Azure storage services and features in their development solutions. It covers Azure Table storage, Azure Cosmos DB, Azure Blob, and developing against relational databases in Azure.
This is the final course of the AI-100 Exam Preparation Learning Path.In this course, we will discuss how to implement various technologies for processing and ingesting your information,we will discuss how to implement Cognitive Services into your application and tie in Cognitive Search to your solution, andwe will discuss tools available for auditing and monitoring plus how to analyze an AI solution and migrate it to another solution if it does not meet requirements.
Welcome to the Implement Azure Security course. This course covers some of the foundational elements for implementing secure applications and data in the cloud as part of a sound security strategy for your organization. We will also cover Azure Key Vault, a service that provides secure storage of your secrets and other sensitive information, followed by the approaches and considerations of disk and data encryption in Azure.
This course is five modules on Implementing and Managing Web APIs in Azure. This first module will provide an overview to give you and understanding of the Web API capabilities available in Microsoft Azure. Then we will cover building API apps, where the API Apps service is, and where it fits within the Azure App Service suite. Next we’ll discuss what the Azure API Management service is and what benefits it provides you for better managing Web APIs in the cloud. Finally, we’ll finish the module with a demo to show off what the API Management portals, both the Publisher Portal and the Developer Portal, look like and where to access them.
In this course, you will learn the ins-and-outs of using Azure Functions to design highly scalable solutions using a serverless design. This course will teach you how to deploy your code as well as how to monitor it once it is in production along with general best practices for writing solutions with Azure Functions.
This course explores the NoSQL storage options available within the Microsoft Azure Cosmos DB database service. Formerly DocumentDB, Azure Cosmos DB is no longer just a Document-based NoSQL store, and it includes support for all 4 primary NoSQL data models (Document, Graph, Key/Value, Column). In addition to learning about NoSQL with Cosmos DB, students will also learn about the cloud-native features that make Cosmos DB a great NoSQL database-as-a-service in the Microsoft Azure cloud.
In this course, we’ll take a look at building Internet of Things solutions using Microsoft Azure IoT Central; a SaaS (Software-as-a-Service) offering. Instruction includes what IoT Central is, discussing the architecture of IoT Central, and how to connect IoT devices to Azure IoT Central.
In this course, we’ll take a look at what Azure IoT Edge as well as what it has to offer for taking cloud capabilities and running that on-premises closer to your Internet of Things (IoT) devices. We’ll also take a look at what it takes to develop your own IoT Edge Modules followed by deploying those to an IoT Edge device.
In this course, we’ll discuss what the Azure IoT Hub service is and what it has to offer for building Internet of Things (IoT) solutions in the Microsoft Azure cloud. We’ll look at the main Azure IoT Hub capabilities and architecture followed by IoT Hub Device Management and Provisioning for connecting IoT Devices to the cloud with Azure IoT Hub. Finally, we’ll look at the overall architecture of integrating IoT Gateways (both Field and Protocol Gateways) into an IoT solution.
In this course, we’ll take a look at what Azure IoT Solution Accelerators offer for more easily building Internet of Things (IoT) solutions in the Microsoft Azure cloud. This includes examining each of the Solution Accelerators (e.g., Remote Monitoring, Connected Factory, Predictive Maintenance, and the IoT Device Simulation).
In this course, we start with an overview of what Azure IoT Sphere is followed by examining different capabilities of Azure Sphere. We'll range from the overall architecture and development, to best practices with the platform, to working with the Azure Sphere devices for deployments and connecting to the Azure Sphere Tenant in the cloud. We’ll conclude this course by going over some topics around Azure Sphere Security.
This course introduces you to Microsoft Cognitive Services and takes you through a gradual journey of features through search, audio, computer vision and language processing services. Along with it’s capabilities, this course explains how to get started developing applications that take advantage of Microsoft Azure Cognitive Service offerings. Once you have clear understanding of all the offerings you can make better decisions on when and how to incorporate Cognitive Services into Enterprise server, desktop, mobile, web, IoT and extended reality (xR/VR/AR/MR) applications.This course will introduce you how to all offerings of Azure Cognitive Services in many different environments, server, web, UWP Store and xR applications.
In this course, the student will be introduced to Docker. We’ll start by understanding the basics of containers and how they came to be. Then, we’ll learn how to install Docker on various platforms. We will cover the components that make up Docker including: The Docker Engine, Docker Images, and Docker Containers. We’ll cover how to containerize an application. We’ll also talk about how networking works with Docker and wrap up with a discussion of how data persistence works within the Docker ecosystem.
In this course, we will cover and introduction to Kubernetes. We will start off by covering what role Kubernetes plays in the container space and how it can simplify container orchestration. We’ll cover scaling, self-healing, load-balancing, and rolling updates. Then, we’ll cover all the ways to install Kubernetes. The remainder of the module with cover the core components of Kubernetes including: Pods, ReplicaSets, Services, and Deployments.
In this hands on course, students will learn about Microsoft Windows Containers. This course starts with an overview of Windows Container platform and its core capabilities. We will then cover use of Microsoft Nano Server and Windows Server Core inside containers. Also covered in the course is usage of Docker CLI (Command Line Interface) alongside PowerShell to perform common tasks like building container images using Dockerfile, running and removing containers. The course wraps up by looking ahead at various application frameworks like ASP.NET 4.5 / ASP.NET Core and IIS Server that are available to run inside Windows Containers.
In this course, we’ll take a look at designing IoT Stream Processing architectures within Microsoft Azure using Azure Stream Analytics and Azure HDInsights services. We’ll also look at using the Lambda Architecture for implementing both Stream Processing and Batch Processing data paths within the same IoT or Big Data solution.
In this course students will gain the knowledge and skills needed to ensure applications hosted in Azure are operating efficiently and as intended. Students will learn how Azure Monitor operates and how to use tools like Log Analytics and Application Insights to better understand what is happening in their application. Students will also learn how to implement autoscale, instrument their solutions to support monitoring and logging, and use Azure Cache and CDN options to enhance the end-user experience.
Welcome to the Running Containers on Azure Course! We'll start off by discussing Microsoft Azure’s managed service offerings for container technologies. We'll then discuss the Azure Container Registry and compare it to other container registry platforms. Next, we’ll go into Azure Container Instances and discuss why and when to use Azure Container Instances followed by how to persist data when running containers in Azure. Finally, we'll cover Azure Kubernetes Service and discuss the advantages that come along with a managed Kubernetes service.
In this module, you will focus on pricing and support models available with Microsoft to include but not limited to Azure subscriptions, planning and managing costs, support options available with Azure, and the service lifecycle in Azure.
In this module you will learn basic cloud concepts to include but not limited to the following: Why Cloud Services?, Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), Software-as-a-Service (SaaS), Public, Private, and Hybrid cloud models.
In this module, you will learn the basics of core services available within Microsoft Azure to include but not limited to Core Azure architectural components, Core Azure Services and Products, Azure Solutions, and Azure management tools.
In this module, you will learn about security, privacy, compliance, and trust with Microsoft Azure. You will become familiar with the following topics: securing network connectivity in Azure, core Azure identity services, security tools and features, Azure governance methodologies, monitoring and reporting in Azure, and privacy, compliance and data protection standards in Azure.
In this lab, you will create a simple URL Shortener application written in C# using Azure Functions serverless compute and the Azure Functions Tools for Visual Studio 2017. The application will make use of Azure Functions Proxies, and host the homepage of the application as a static web page in Azure Storage. The app also uses Azure Storage Tables for the backend data store for the URLs and their shortened address codes.
In this lab, you will create a knowledge base in QnA Maker and connect it to a bot using the Azure Bot Service. Then you will interact with the bot using Teams — one of many popular services with which bots built with the Azure Bot Service can integrate.
In this lab, you will create a new Azure Function that exposes an HTTP endpoint to enable the function to be triggered on-demand. The HTTP endpoint accepts two query string parameters from the HTTP request. The function outputs a calculated value based on the input parameters.
In this lab, you will create, deploy, and configure an Azure Web App using Java, CosmosDB (DocumentDB), Azure Active Directory, and Application Insights. Your first exercise will be to create a development environment where you can create and debug Java code. From there, you will integrate more Azure services and technologies to complete the cloud environment, authentication, continuous deployment, and diagnostics for your application.
In this lab, you will gain hands-on experience building and deploying an enterprise web application in Azure by building a simple event management application written in ASP.NET MVC that uses Azure Web Apps, SQL Database, Azure Active Directory and Redis Cache.
You are the leader of a group of climate scientists who are concerned about the dwindling polar-bear population in the Arctic. As such, your team has placed hundreds of motion-activated cameras at strategic locations throughout the region. Rather than manually examine each photograph to determine whether it contains a polar bear, you have been challenged to devise an automated system that processes data from these cameras in real time and displays an alert on a map when a polar bear is photographed. You need a solution that incorporates real-time stream processing to analyze raw data for potential sightings, and one that incorporates artificial intelligence (AI) and machine learning to determine with a high degree of accuracy whether a photo contains a polar bear. And you need it fast, because climate change won’t wait.In this lab, you will build such a system using Microsoft Azure and Microsoft Cognitive Services. Specifically, you will use an Azure IoT hub to ingest streaming data from simulated cameras, Azure Storage to store photographs, Azure Stream Analytics to process real-time data streams, Azure Functions to process output from Stream Analytics, Microsoft’s Custom Vision Service to analyze photographs for polar pears, Microsoft Power BI to build a dashboard for visualizing results, and Azure SQL Database as a data source for Power BI.
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 create multiple Azure Cosmos DB containers. Some of the containers will be unlimited and configured with a partition key, while others will be fixed-sized. You will then use the SQL API and .NET SDK to query specific containers using a single partition key or across multiple partition keys.
In this lab you will create an Azure SQL Database using the Azure Portal and connect to it using SQL Server Management Studio. You will then migrate a SQL Server database hosted on a virtual machine to an Azure SQL Database.
In this lab, you will create a Linux virtual machine running in Azure, and connect to it using SSH. You will then delete the virtual machine, and clean up associated resources.
In this lab, you will create a Windows virtual machine running in Azure, and connect to it using Remote Desktop. You will then delete the virtual machine, and clean up associated resources.
In this lab, you will setup, configure, and deploy an Azure IoT Edge Device that communicates with Azure IoT Hub. The IoT Edge Device will be a simulated device running in an Ubuntu Linux Virtual Machine.
In this lab we'll guide you through the steps to deploy a request splitting ambassador that will split 10% of the incoming HTTP requests to an experimental server and the rest to a primary web server using Azure Kubernetes Service (AKS). This pattern is commonly used for testing new features or user experience to a small subset of users.
In this lab, an AKS cluster is deployed using the Azure CLI. A multi-container application consisting of web front end and a Redis instance is then run on the cluster. Once completed, the application is accessible over the internet.
In this lab, you will create an Azure Storage Account (Blobs, Tables, Queues) and access it by using a Java-based web app that uses it for storing data and images. You will be able to use the Azure Storage Explorer to examine the storage account contents while using the application to see how it works.
In this lab, an Azure Virtual Machine disk will be encrypted using the following steps:Deploy a VM into Azure that is not encryptedObtain and run the Azure Disk Encryption Prerequisites Azure PowerShell scriptEncrypt your virtual machines
In this lab, you will create a virtual network that will allow the virtual machines you create to securely connect with each other. You will then create two virtual machines and specify the virtual network configuration and the availability set configuration along with storage for the virtual machine.
In this lab, you will create an Azure Web App and a SQL Database and configure the popular content management system (CMS) Orchard CMS. You will then configure the web app to automatically scale based on actual CPU usage.
In this lab, you will create an Azure Web App in the portal and add a staging slot. Then, using Visual Studio you will create a .Net application. You will read environmental variables from a file, then remove the dependency on that file with application settings in Azure. You will view default logs using log streaming in Azure and then create custom log messages in the application. You will explore slot deployment by deploying these changes to Stage and then swapping them to Production.
In this lab, you will create a Web API using ASP.NET MVC that will then be deployed into Azure API Apps. You will also integrate Swagger using the Swashbuckle NuGet package to automatically generate usage documentation for the Web API. From there you will setup a new API Management Service within Azure, and publish a custom Web API deployed to an Azure API App to be a Managed API.
In this lab, you will learn how to use Visual Studio Code to author an ARM Template that declares the Azure Resources necessary to host an Azure Web App, Azure SQL Database, and Azure Application Insights.
In this lab, you will be introduced to basic concepts for developing with Azure Storage using Visual Studio and C#.
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 create, deploy, and configure an application using Java and the Azure Service Bus to demonstrate the use of messaging with queues. Your first exercise will be to create a development environment where you can create and debug Java code. After that, you will create a Service Bus queue, an Azure Function, and an Azure Cosmos DB database to demonstrate the full message cycle. The Java web application, running in Docker on your development machine, will use the Service Bus queue to communicate with the Azure function which will process the message and finally save the result into the Cosmos DB database.
In this lab, the student will learn the basics of messaging patterns between software systems and how to use the Azure Service Bus as a messaging solution.
In this lab, you will create an Azure Traffic Manager profile, and use it to distribute traffic between 3 Azure Web App endpoints deployed to different global locations. You will learn how to use the Azure portal to configure the different ways in which Traffic Manager distributes traffic between endpoints, and how to configure endpoint health checks and test endpoint failover, for high-availability applications.
In this lab, you will build and run container based on IIS Server, ASP.NET 4.5 and ASP.Net Core Frameworks. You will use Dockerfile to create container image and then use Docker CLI commands to launch thecontainers. Finally, you will work work with docker commands to access container logs and stats including CPU and memory.
In this lab, you will use Visual Studio and ASP.NET to learn how to use Cosmos DB as a backend for an MVC application. You will learn how to programmatically read and write data, create and call a user-defined functions as well as understand management capabilities such as users and permissions, monitoring and scalability options.
In this lab, you will setup and configure messaging for implementing the Lambda Architecture within Microsoft Azure utilizing Azure Stream Analytics and Azure IoT Hub services. IoT events will also be sent to Azure IoT Hub using a Simulated IoT Device written as a C# console application.
In this lab, you will use the Azure Resource Manager (ARM) REST API, via the Azure Resource Explorer, to provision and Azure Function App hosted on an App Service Plan using Consumption plan pricing. Then you will provision a new Azure Storage Account, and update it's configuration to use Read-Access Geo-Redundant Storage to replicate the data stored to a read-only, secondary Azure Region / Location.
In this lab, you will learn how to configure and manage an Azure Cosmos DB Account (formerly Azure DocumentDB), including how to query and manage JSON documents within a Collection. Among the topics covered are using SQL language syntax to perform document queries that return JSON results, and implementing and testing global data replication and fail over.
In this lab, you will perform several maintenance operations on an existing IaaS application. All operations will be carried out by making direct calls to the Azure Resource Manager REST API, using the Resource Explorer tool. This lab will automatically provision several virtual machines and will take 15-25 minutes to fully start.
In this Lab, you will use the Nerd Dinner Application. Nerd Dinner is an Open Source ASP.NET MVC Project that helps nerds and computer people plan get-togethers. You can see the site running LIVE at http://www.nerddinner.com. You will move the application DB to Azure SQL instance and add the Docker support to the application to run the application in Azure Container Instances.
In this lab, you will query an Azure Cosmos DB database instance using the SQL language. You will use features common in SQL such as projection using SELECT statements and filtering using WHERE clauses. You will also get to use features unique to Azure Cosmos DB’s SQL API such as projection into JSON, intra-document JOIN and filtering to a range of partition keys.
In this lab, you will use the .NET SDK to tune an Azure Cosmos DB request to optimize performance of your application.
In this lab, you will create an Azure Function that monitors a blob container in Azure Storage for new images, and then performs automated analysis of the images using the Microsoft Cognitive Services Computer Vision API. Specifically, The Azure Function will analyze each image that is uploaded to the container for adult or racy content and create a copy of the image in another container. Images that contain adult or racy content will be copied to one container, and images that do not contain adult or racy content will be copied to another. In addition, the scores returned by the Computer Vision API will be stored in blob metadata.
In this lab, you will learn how to make direct calls to the Azure Resource Manager REST API. There are various different tools available to make these API calls Each exercise focuses on a different tool, and on different features of the REST API.