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In this course, students will learn the fundamentals of database concepts in a cloud environment, get basic skilling in cloud data services, and build their foundational knowledge of cloud data services within Microsoft Azure. Students will identify and describe core data concepts such as relational, non-relational, big data, and analytics, and explore how this technology is implemented with Microsoft Azure. They will explore the roles, tasks, and responsibilities in the world of data. The students will explore relational data offerings, provisioning and deploying relational databases, and querying relational data through cloud data solutions with Microsoft Azure. They will explore non-relational data offerings, provisioning and deploying non-relational databases, and non-relational data stores with Microsoft Azure. Students will explore the processing options available for building data analytics solutions in Azure. They will explore Azure Synapse Analytics, Azure Databricks, and Azure HDInsight. Students will learn what Power BI is, including its building blocks and how they work together.
A container is a virtualization technology used to implement scale-out applications that require greater efficiency and scalability. This course starts with covering Docker technology and how to deploy your .NET applications in Docker containers. You will learn about the Microservice architecture and how containers are used in this model. Finally, the training will make you familiar will the different container services offered by Azure like Azure Container Instances and Azure Kubernetes Service.
In this course you will learn about using Azure DevOps, formerly known as Visual Studio Team Services (VSTS), to manage the application development lifecycle. This training will show you the role of the project manager, developer and tester in this process and how DevOps can improve the efficiency and code quality of your team. You will perform project management, source control (no real coding skill required for the exercise), testing and build automation with Azure DevOps.
In this course students will learn to build Docker containers on both Windows and Linux. They will use Docker Compose and Docker Swarm to manage and maintain multi-layered applications on different container hosts. Students will deploy a Kubernetes cluster and learn how Kubernetes handles the deployment of applications across various nodes. Finally, students will take a look at the Azure services related to container management and maintenance.
Microsoft Endpoint Manager is a single, integrated endpoint management platform for all your endpoints. It integrates Configuration Manager and Microsoft Intune. This course focuses on Intune: a cloud-based service in the enterprise mobility management (EMM) space that helps enable your workforce to be productive while keeping your corporate data protected.
A modern data warehouse lets you bring together all your data at any scale easily. It offers insights through analytical dashboards, operational reports and advanced analytics. Microsoft Azure offers a broad range of services like Azure Data Factory, Azure Data Lake, Azure Databricks and Azure Synapse Analytics helping you build your data warehouse in the cloud. This training will cover all aspects of designing and implementing a data warehouse on Microsoft Azure. Participants will leave the training with hands-on experience with all Microsoft Azure services to explore, prepare, manage and serve data for immediate BI or machine-learning needs.
In this two-day course the basic concepts of Machine Learning for citizen data science are introduced. A number of tools are introduced that can be used to create and deploy ML models without a lot of Machine Learning or coding knowledge in Microsoft Azure. Azure Machine Learning Service allows your models to be created automatically (Automated Machine Learning), or you can create your ML pipelines using a drag-n-drop approach (Designer). Cognitive Services are shown as well. These are AI services and cognitive APIs that you can easily use to build intelligent apps, without the need to have AI knowledge. Finally, you will have a look at the AI features that are available in Power BI, such as built-in AI visuals, and the possibility to use a ML model that you created in Azure Machine Learning or Cognitive services, in Power Query.
Data science converts data into insights by applying techniques from the field of artificial intelligence and machine learning. This field has received a lot of attention lately, resulting in a lot of possible techniques to tackle this problem. In this training you will gradually dive deeper in the use of Python and the Azure stack to apply machine learning on business data.