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.
Design a modernization plan to move services from on-premises to the cloud by leveraging cloud, web, and mobile services, secured by Azure Active Directory.
Design a hybrid cloud architecture using a combination of the Azure public cloud and Azure Stack. Recommend the appropriate hybrid cloud systems based on customer requirements.
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.
Set up and configure a scale-out media processing architecture using Azure Batch. Use High Performance Computing (HPC) techniques with less code and accomplish tasks declaratively.
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 a Windows Virtual Desktop solution using Microsoft 365 and Azure technologies. Determine requirements needed for multi-user licensing and AAD security, and design a WVD 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.
Build a smart city solution by managing intelligent devices with IoT Hub and running the IoT Remote Monitoring starter solution to improve traffic and public transportation.
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.
Deploy and configure SAP NetWeaver on Azure infrastructure and describe the proper configuration of Azure solution components to run SAP applications.
Learn to architect a comprehensive oil and gas manufacturing IoT solution that is secured end-to-end 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 Azure Security Center and Microsoft Compliance Manager to ensure a secure and privacy-focused cloud-based architecture that follows compliance standards.
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.
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Effective November 30, 2020, the following MCWs will be archived: Business continuity and disaster recovery, Enterprise-ready cloud, Modernizing Data Analytics with SQL Server 2019, OSS PaaS and DevOps, and Real-time data with Azure Database for PostgreSQL Hyperscale. Materials remain available through November, but are no longer being maintained.
Data Platform upgrade and migration is now Migrating Oracle to Azure SQL and PostgreSQL! Make sure to take a peek at the new and improved content.