Microsoft Cloud Workshop (MCW) is a hands-on community development experience.Use our customer-ready content to host workshops that foster cloud learning and adoption. Contribute your own content and feedback to add to a robust database of training guides for deploying advanced Azure workloads on the Microsoft Cloud Platform.
Microsoft Cloud Workshop (MCW) is a hands-on community development experience.Use our customer-ready content to host events with customers and partners, or contribute your own content and feedback to add to a robust database of training guides for deploying advanced Azure workloads on the Microsoft Cloud Platform.
Microsoft Cloud Workshop library
Use the following filters to search our database of Microsoft Cloud Workshop materials. Each workshop includes presentation decks, trainer and student guides, and hands-on lab guides.
Learn to architect and implement a business process automation solution leveraging Azure Cognitive Services in a healthcare scenario. Use Form Recognizer to extract data, Speech services to transcribe audio, and analyze text to extract needed information.
Design a modernization plan to move legacy on-premises applications and infrastructure to Azure. Update applications to take advantage of serverless and cloud services.
Learn to design and build an end-to-end solution using Azure Synapse Analytics. Utilize data loading, data preparation, data transformation and data serving. Perform machine learning and handling of both batch and real-time data.
Build a complete machine learning model in Azure Databricks and deploy a web app for predicting flight delay. Train the model to report data.
Build resilient architectures by designing for converting/extending an existing IaaS deployment to account for resiliency and high availability.
Using DevOps best practices, build a proof of concept to transform a PaaS application to a container-based application with multi-tenant web app hosting.
Combine pre-built AI models with custom AI services to create intelligent solutions atop unstructured text data by designing and implementing a text analytics pipeline.
Set up and configure continuous delivery within Azure to reduce manual errors using Azure Resource Manager templates, Azure DevOps, and Git repositories for source control.
Design a data pipeline solution leveraging Cosmos DB for scalable ingest and global distribution. Use Azure Databricks Delta with a modern data warehouse to reduce risk.
Set up, configure, plan, and design secure virtual networks in Azure with multiple subnets to filter and control network traffic.
Learn to setup and configure a hybrid identity solution that integrates an existing on-premises identity solution with Azure. Create a virtual network and provision subnets, create route tables with required routes, build a management jump box, configure firewalls to control traffic flow, and configure site-to-site connectivity.
Design an Azure Virtual Desktop solution using Microsoft 365 and Azure technologies. Determine requirements needed for multi-user licensing and AAD security, and design an AVD solution utilizing virtual machines with scalability to handle 24x7 operations.
Build a cloud processing and machine learning solution for IoT data using Azure services. Design a solution for ingesting and preparing data, detecting anomalies, and deploying a machine learning model with real-time scoring of a predictive maintenance model.
Employ real-time analytics without IoT as you enable intelligent conversation in a real-time chat pipeline enabled by machine learning to visualize customer sentiment.
Design an end-to-end IoT solution implementing device registration with the IoT Hub Device Provisioning Service and visualizing hot data with Power BI.
Learn how to design and implement an end-to-end Azure IoT solution using Azure Digital Twins in a supply chain environment.
Design a migration strategy for both Windows and Linux servers, including virtual and physical services, as well as databases, from on-premises to Azure.
Combine Azure Databricks with Azure Machine Learning service to build, train, and deploy machine learning and deep learning models. Construct deep learning models for NLP in text classification, and compare data with PyTorch and Keras.
Conduct a site analysis for a customer to compare cost, performance, and level of effort required to migrate from Oracle to SQL Server or PostgreSQL.
Develop a plan for migrating on-premises virtual machines and SQL Server 2008 R2 databases into a combination of IaaS and PaaS services in Azure and enable advanced SQL features to improve security and performance.
Learn to design and implement end-to-end solutions that fully operationalize deep learning models from model creation, application packaging, model deployment and application deployment in one unified repeatable, pipeline.
Deploy, configure, and implement an end-to-end secure and PCI compliant solution for e-commerce that is based on Azure App Services, Azure Active Directory, and Azure DevOps.
Learn to evaluate Microsoft's catalog of PaaS and SaaS-based IoT products to determine an optimal combination. Design and implement a solution that simplifies IoT device management and reporting, and deploy a trained predictive maintenance Machine Learning model in near real-time.
Provision a highly available deployment of SAP HANA in Azure, identifying infrastructure components necessary to support a clustered deployment and testing failover scenarios.
Learn to to extract SAP data into a single dashboard for reporting and forecasting in a supply chain. Design the use of SAP data to predict trends and provide recommendations to customers.
Learn to architect a comprehensive oil and gas manufacturing IoT solution that is secured following best practices. Provide guidance to your customer on defining lifecycles from deployment, maintenance, planned obsolescence, and decommissioning of devices and how Azure supports this.
Implement a series of Azure Functions that independently scale and break down business logic to discrete components, allowing customers to pay only for the services they use.
We welcome feedback and comments from Microsoft SMEs & learning partners who deliver MCWs.
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Effective October 1, 2021, MCWs Intelligent analytics, Machine learning, and SQL Server hybrid cloud will be retired. Content will be available through September, but the workshops are no longer being maintained or updated.
Implementing Windows Virtual Destop in the enterprise is now Implementing Azure Virtual Destop in the enterprise! Same great content with an updated title.