Empowering Disaster Recovery for Azure VMs with Azure Site Recovery and Terraform
Discover how to ensure business continuity and achieve disaster recovery for your Azure Virtual Machines with ease. Learn how to integrate seamlessly with Azure Site Recovery using Terraform, providing a simple, secure, and cost-effective way to replicate VMs across regions. Stay prepared for any outage with a failover process that keeps your apps running, all while paying only for storage and traffic to the secondary region. Don't miss this opportunity to fortify your VM infrastructure and maintain uninterrupted operations!Azure Course Blueprints
Overview The Course Blueprint is a comprehensive visual guide to the Azure ecosystem, integrating all the resources, tools, structures, and connections covered in the course into one inclusive diagram. It enables students to map out and understand the elements they've studied, providing a clear picture of their place within the larger Azure ecosystem. It serves as a 1:1 representation of all the topics officially covered in the instructor-led training. Formats available include PDF, Visio, Excel, and Video. Links: Each icon in the blueprint has a hyperlink to the pertinent document in the learning path on Learn. Layers: You have the capability to filter layers to concentrate on segments of the course by modules. I.E.: Just day 1 of AZ-104, using filters in Visio and selecting modules 1-3 Integration: The Visio Template+ for expert courses like SC-100 and AZ-305 includes an additional layer that enables you to compare SC-100, AZ-500, and SC-300 within the same diagram. Similarly, you can compare any combination of AZ-305, AZ-700, AZ-204, and AZ-104 to identify differences and study gaps. Since SC-300 and AZ-500 are potential prerequisites for the expert certification associated with SC-100, and AZ-204 or AZ-104 for the expert certification associated with AZ-305, this comparison is particularly useful for understanding the extra knowledge or skills required to advance to the next level. Advantages for Students Defined Goals: The blueprint presents learners with a clear vision of what they are expected to master and achieve by the course’s end. Focused Learning: By spotlighting the course content and learning targets, it steers learners’ efforts towards essential areas, leading to more productive learning. Progress Tracking: The blueprint allows learners to track their advancement and assess their command of the course material. Topic List: A comprehensive list of topics for each slide deck is now available in a downloadable .xlsx file. Each entry includes a link to Learn and its dependencies. Download links Associate Level PDF Visio Released Updated Contents Video Overview Demo Deploy AZ-104 Azure Administrator Associate Blueprint Template 12/14/2023 10/28/2024 Contents Module 01 Microsoft Trainer Demo Deploy AZ-204 Azure Developer Associate Blueprint Template 11/05/2024 11/11/2024 Contents Microsoft Trainer Demo Deploy AZ-500 Azure Security Engineer Associate Blueprint Template+ 01/09/2024 10/10/2024 Contents Microsoft Trainer Demo Deploy AZ-700 Azure Network Engineer Associate Blueprint Template 01/25/2024 11/04/2024 Contents Microsoft Trainer Demo Deploy SC-300 Identity and Access Administrator Associate Blueprint Template 10/10/2024 Contents Specialty PDF Visio Released Updated AZ-140 Azure Virtual Desktop Specialty Blueprint Template 01/03/2024 02/27/2025 Contents Expert level PDF Visio Released Updated AZ-305 Designing Microsoft Azure Infrastructure Solutions Blueprint Template+ AZ-104 AZ-204 AZ-700 AZ-140 05/07/2024 02/05/2025 Contents Microsoft Trainer Demo Deploy SC-100 Microsoft Cybersecurity Architect Blueprint [PDF] Template+ AZ-500 SC-300 10/10/2024 Contents Skill based Credentialing PDF Visio Released Updated AZ-1002 Configure secure access to your workloads using Azure virtual networking Blueprint Blueprint Template 05/27/2024 Contents AZ-1003 Secure storage for Azure Files and Azure Blob Storage Blueprint Template 02/07/2024 02/05/2024 Contents Benefits for Trainers: Trainers can follow this plan to design a tailored diagram for their course, filled with notes. They can construct this comprehensive diagram during class on a whiteboard and continuously add to it in each session. This evolving visual aid can be shared with students to enhance their grasp of the subject matter. Introduction to Course Blueprint for Trainers [10 minutes + comments] Real life demo AZ-104 Advanced Networking section [3 minutes] Visio stencils Azure icons - Azure Architecture Center | Microsoft Learn AZ-104 Overview of Mod 01 using Azure Course Blueprint __ Practical Scenario Demo with Demo Deploy To enhance your learning experience, we're linking Demo Deploy with Azure Course Blueprints. This tool will allow you to: See Practical Applications: Understand how different portions of the course content are applied in real-world scenarios. Contextual Learning: Visualize where each topic fits within the larger Azure ecosystem and the specific context of the course. This integration ensures a comprehensive and practical approach to learning, making it easier to grasp and apply the concepts covered in the course. Microsoft Trainer Demo Deploy ___ Subscribe if you want to get notified of any update like new releases or updates. My email ilan.nyska@microsoft.com LinkedIn https://www.linkedin.com/in/ilan-nyska/ Please consider sharing your anonymous feedback <-- Thank you for your support!Getting started with the NetApp Connector for Microsoft M365 Copilot and Azure NetApp Files
Imagine a world where your on-premises and enterprise cloud files seamlessly integrate with Microsoft Copilot unleashing AI on your Azure NetApp Files enterprise data, and making your workday smoother and more efficient. Welcome to the future with the NetApp Connector for Microsoft Copilot!Demystifying Azure OpenAI Networking for Secure Chatbot Deployment
Embark on a technical exploration of Azure's networking features for building secure chatbots. In this article, we'll dive deep into the practical aspects of Azure's networking capabilities and their crucial role in ensuring the security of your OpenAI deployments. With real-world use cases and step-by-step instructions, you'll gain practical insights into optimizing Azure and OpenAI for your projects.AI for Operations
Solutions idea This solution series shows some examples of how Azure OpenAI and its LLM models can be used on Operations and FinOps issues. With a view to the use of models linked to the Enterprise Scale Landing Zone, the solutions shown, which are available on a dedicated GitHub, are designed to be deployed within a dedicated subscription, in the examples called ‘OpenAI-CoreIntegration’. The examples we are going to list are: SQL BPA AI Enhanced Azure Update Manager AI Enhanced Azure Cost Management AI Enhanced Azure AI Anomalies Detection Azure OpenAI Smart Doc Creator Enterprise Scale AI for Operations Landing Zone Design Architecture SQL BPA AI Enhanced Architecture This LogApp is an example of integrating ARC SQL practices assessment results with OpenAI, creating an HTML report and CSV file send via Email with OpenAI comment of Severity High and/or Medium results based on the actual Microsoft Documentation. Dataflow Initial Trigger Type: Recurrence Configuration: Frequency: Weekly Day: Monday Time: 9:00 AM Time Zone: W. Europe Standard Time Description: The Logic App is triggered weekly to gather data for SQL Best Practice Assessments. Step 1: Data Query Action: Run_query_and_list_results Description: Executes a Log Analytics query to retrieve SQL assessment results from monitored resources. Output: A dataset containing issues classified by severity (High/Medium). Step 2: Variable Initialization Actions: Initialize_variable_CSV: Initializes an empty array to store CSV results. Open_AI_API_Key: Sets up the API key for Azure OpenAI service. HelpLinkContent: Prepares a variable to store useful links. Description: Configures necessary variables for subsequent steps. Step 3: Process Results Action: For_eachSQLResult Description: Processes the query results with the following sub-steps: Condition: Checks if the severity is High or Medium. OpenAI Processing: Sends structured prompts to the GPT-4 model for recommendations on identified issues. Parses the JSON response to extract specific insights. CSV Composition: Creates an array containing detailed results. Step 4: Report Generation Actions: Create_CSV_table: Converts processed data into a CSV format. Create_HTML_table: Generates an HTML table from the data. ComposeMailMessage: Prepares an HTML email message containing the results and a link to the report. Description: Formats the data for sharing. Step 5: Saving and Sharing Actions: Create_file: Saves the HTML report to OneDrive. Send_an_email_(V2): Sends an email with the reports attached (HTML and CSV). Post_message_in_a_chat_or_channel: Shares the results in a Teams channel. Description: Distributes the reports to defined recipients. Components Azure OpenAI service is a platform provided by Microsoft that offers access to powerful language models developed by OpenAI, including GPT-4, GPT-4o, GPT-4o mini, and others. The service is used in this scenario for all the natural language understanding and generating communication to the customers. Azure Logic Apps is a cloud platform where you can create and run automated workflows with little to no code. Azure Logic Apps Managed Identities allow to authenticate to any resource that supports Microsoft Entra authentication, including your own applications. Azure Bing Web Search enables safe, ad-free, location-aware search results, surfacing relevant information from billions of web documents. Help your users find what they're looking for from the world-wide-web by harnessing Bing's ability to comb billions of webpages, images, videos, and news with a single API call. Azure ARC SQL Server enabled by Azure Arc extends Azure services to SQL Server instances hosted outside of Azure: in your data center, in edge site locations like retail stores, or any public cloud or hosting provider. SQL Best Practices Assessment feature provides a mechanism to evaluate the configuration of your SQL Server instance. Azure Monitor is a comprehensive monitoring solution for collecting, analyzing, and responding to monitoring data from your cloud and on-premises environments. Azure Kusto Queryis a powerful tool to explore your data and discover patterns, identify anomalies and outliers, create statistical modeling, and more Potential use cases SQL BPA AI Enhanced exploits the capabilities of the SQL Best Practice Assessment service based on Azure ARC SQL Server. The collected data can be used for the generation of customised tables. The solution is designed for customers who want to enrich their Assessment information with Generative Artificial Intelligence. Azure Update Manager AI Enhanced Architecture This LogApp solution example retrieves data from the Azure Update Manager service and returns an output processed by generative artificial intelligence. Dataflow Initial Trigger Type: Recurrence Trigger Frequency: Monthly Time Zone: W. Europe Standard Time Triggers the Logic App at the beginning of every month. Step 1: Initialize API Key Action: Initialize Variable Variable Name: Api-Key Step 2: Fetch Update Status Action: HTTP Request URI: https://management.azure.com/providers/Microsoft.ResourceGraph/resources Query: Retrieves resources related to patch assessments using patchassessmentresources. Step 3: Parse Update Status Action: Parse JSON Content: Response body from the HTTP request. Schema: Extracts details such as VM Name, Patch Name, Patch Properties, etc. Step 4: Process Updates For Each: Body('Parse_JSON')?['data'] Iterates through each item in the parsed update data. Condition: If Patch Name is not null and contains "KB": Action: Format Item Parses individual update items for VM Name, Patch Name, and additional properties. Action: Send to Azure OpenAI Description: Sends structured prompts to the GPT-4 model Headers: Content-Type: application/json api-key: @variables('Api-Key') Body: Prompts Azure OpenAI to generate a report for each virtual machine and patch, formatted in Italian. Action: Parse OpenAI Response Extracts and formats the response generated by Azure OpenAI. Action: Append to Summary and CSV Adds the OpenAI-generated response to the Updated Summary array. Appends patch details to the CSV array. Step 5: Finalize Report Action: Create Reports (I, II, III) Formats and cleans the Updated Summary variable to remove unwanted characters. Action: Compose HTML Email Content Constructs an HTML email with the following: Report summary generated using OpenAI. Disclaimer about possible formatting anomalies. Company logo embedded. Step 6: Generate CSV Table Action: Converts the CSV array into a CSV format for attachment. Step 7: Send E-Mail Action: Send Email Recipient: user@microsoft.com Subject: Security Update Assessment Body: HTML content with report summary. Attachment: Name: SmartUpdate_<timestamp>.csv Content: CSV table of update details. Components Azure OpenAI service is a platform provided by Microsoft that offers access to powerful language models developed by OpenAI, including GPT-4, GPT-4o, GPT-4o mini, and others. The service is used in this scenario for all the natural language understanding and generating communication to the customers. Azure Logic Apps is a cloud platform where you can create and run automated workflows with little to no code. Azure Logic Apps Managed Identities allow to authenticate to any resource that supports Microsoft Entra authentication, including your own applications. Azure Update Manager is a unified service to help manage and govern updates for all your machines. You can monitor Windows and Linux update compliance across your machines in Azure and on-premises/on other cloud platforms (connected by Azure Arc) from a single pane of management. You can also use Update Manager to make real-time updates or schedule them within a defined maintenance window. Azure Arc Server lets you manage Windows and Linux physical servers and virtual machines hosted outside of Azure, on your corporate network, or other cloud provider. Potential use cases Azure Update Manager AI Enhanced is an example of a solution designed for all those situations where the IT department needs to manage and automate the telling of information in a readable format on the status of updates to its infrastructure thanks to an output managed by generative artificial intelligence Azure Cost Management AI Enhanced Architecture This LogApp solution retrieves consumption data from the Azure environment and generates a general and detailed cost trend report on a scheduled basis. Dataflow Initial Trigger Type: Manual HTTP Trigger The Logic App is triggered manually using an HTTP request. Step 1: Set Current Date and Old Date Action: Set Actual Date Current date is initialized to @utcNow('yyyy-MM-dd'). Example Value: 2024-11-22. Action: Set Actual Date -30 Old date is set to 30 days before the current date. Example Value: 2024-10-23. Action: Set old date -30 Sets the variable currentdate to 30 days prior to the old date. Example Value: 2024-09-23. Action: Set old date -60 Sets the variable olddate to 60 days before the current date. Example Value: 2024-08-23. Step 2: Query Cost Data Action: Query last 30 days Queries Azure Cost Management for the last 30 days. Example Data Returned:json{ "properties": { "rows": [ ["Virtual Machines", 5000], ["Databases", 7000], ["Storage", 3000] ] } } Copia codice Action: Query -60 -30 days Queries Azure Cost Management for 30 to 60 days ago. Example Data Returned:json{ "properties": { "rows": [ ["Virtual Machines", 4800], ["Databases", 6800], ["Storage", 3050] ] } } Copia codice Step 3: Download Detailed Reports Action: Download_report_actual_month Generates and retrieves a detailed cost report for the current month. Action: Download_report_last_month Generates and retrieves a detailed cost report for the previous month. Step 4: Process and Store Reports Action: Actual_Month_Report Parses the JSON from the current month's report. Retrieves blob download links for the detailed report. Action: Last_Month_Report Parses the JSON from the last month's report. Retrieves blob download links for the detailed report. Action: Create_ActualMonthDownload and Create_LastMonthDownload Initializes variables to store download links. Action: Get_Actual_Month_Download_Link and Get_Last_Month_Download_Link Iterates through blob data and assigns the download link variables. Step 5: Generate Questions for OpenAI Action: Set_Question Prepares the first question for Azure OpenAI: "Describe the key differences between the previous and current month's costs, and create a bullet-point list detailing these differences in Euros." Action: Set_Second_Question Prepares a second question for Azure OpenAI: "Briefly describe in Italian the major cost differences between the two months, rounding the amounts to Euros." Step 6: Send Questions to Azure OpenAI Action: Passo result to OpenAI Sends the first question to OpenAI for generating detailed insights. Action: Get Description from OpenAI Sends the second question to OpenAI for a brief summary in Italian. Step 8: Process OpenAI Responses Action: Parse_JSON and Parse_JSON_Second_Question Parses the JSON response from OpenAI for both questions. Retrieves the content of the generated insights. Action: For_each_Description Iterates through OpenAI's responses and assigns the description to a variable DescriptionOutput. Step 9: Compose and send E-Mail Action: Compose_Email Composes an HTML email including: Key insights from OpenAI. Links to download the detailed reports. Example Email Content: Azure automated cost control system: - Increase of €200 in Virtual Machines. - Reduction of €50 in Storage. Download details: - Current month: [Download Report] - Previous month: [Download Report]. Action: Send_an_email_(V2) Sends the composed email. Components Azure OpenAI service is a platform provided by Microsoft that offers access to powerful language models developed by OpenAI, including GPT-4, GPT-4o, GPT-4o mini, and others. The service is used in this scenario for all the natural language understanding and generating communication to the customers. Azure Logic Apps is a cloud platform where you can create and run automated workflows with little to no code. Azure Logic Apps Managed Identities allow to authenticate to any resource that supports Microsoft Entra authentication, including your own applications. Potential use cases Azure Cost Management AI Enhanced is an example of a solution designed for those who need to programme the generation of reports related to FinOps topics with the possibility to customise the output and send the results via e-mail or perform a customised upload. Azure AI Anomalies Detection Architecture This LogApp solution leverages Azure Monitor's native machine learning capabilities to retrieve anomalous data within application logs. These will then be analysed by OpenAI. Dataflow Initial Trigger Type: Recurrence Trigger Frequency: Monthly Time Zone: W. Europe Standard Time Triggers the Logic App at the beginning of every month. Step 1: Initialize API Key Action: Initialize Variable Variable Name: Api-Key Step 2: Fetch Update Status Action: HTTP Request URI: https://management.azure.com/providers/Microsoft.ResourceGraph/resources Query: Retrieves resources related to patch assessments using patchassessmentresources. Step 3: Parse Update Status Action: Parse JSON Content: Response body from the HTTP request. Schema: Extracts details such as VM Name, Patch Name, Patch Properties, etc. Step 4: Process Updates For Each: @body('Parse_JSON')?['data'] Iterates through each item in the parsed update data. Condition: If Patch Name is not null and contains "KB": Action: Format Item Parses individual update items for VM Name, Patch Name, and additional properties. Action: Send to Azure OpenAI Description: Sends structured prompts to the GPT-4 model. Headers: Content-Type: application/json api-key: @variables('Api-Key') Body: Prompts Azure OpenAI to generate a report for each virtual machine and patch, formatted in Italian. Action: Parse OpenAI Response Extracts and formats the response generated by Azure OpenAI. Action: Append to Summary and CSV Adds the OpenAI-generated response to the Updated Summary array. Appends patch details to the CSV array. Step 5: Finalize Report Action: Create Reports (I, II, III) Formats and cleans the Updated Summary variable to remove unwanted characters. Action: Compose HTML Email Content Constructs an HTML email with the following: Report summary generated using OpenAI. Disclaimer about possible formatting anomalies. Company logo embedded. Step 6: Generate CSV Table Action: Converts the CSV array into a CSV format for attachment. Step 7: Send Notifications Action: Send Email Recipient: user@microsoft.com Subject: Security Update Assessment Body: HTML content with report summary. Attachment: Name: SmartUpdate_<timestamp>.csv Content: CSV table of update details. Components Azure OpenAI service is a platform provided by Microsoft that offers access to powerful language models developed by OpenAI, including GPT-4, GPT-4o, GPT-4o mini, and others. The service is used in this scenario for all the natural language understanding and generating communication to the customers. Azure Logic Apps is a cloud platform where you can create and run automated workflows with little to no code. Azure Logic Apps Managed Identities allow to authenticate to any resource that supports Microsoft Entra authentication, including your own applications. Azure Monitor is a comprehensive monitoring solution for collecting, analyzing, and responding to monitoring data from your cloud and on-premises environments. Azure Kusto Queryis a powerful tool to explore your data and discover patterns, identify anomalies and outliers, create statistical modeling, and more Potential use cases Azure AI Anomalies Detection is an example of a solution that exploits the Machine Learning capabilities of Azure Monitor to diagnose anomalies within application logs that will then be analysed by Azure OpenAI. The solution can be customized based on Customer requirements. Azure OpenAI Smart Doc Creator Architecture This Function App solution leverages the Azure OpenAI LLM Generative AI to create a docx file based on the Azure architectural information of a specific workload (Azure Metadata based). The function exploits the 'OpenAI multi-agent' concept. Dataflow Step 1: Logging and Configuration Setup Initialize Logging: Advanced logging is set up to provide debug-level insights. Format includes timestamps, log levels, and messages. Retrieve OpenAI Endpoint: QUESTION_ENDPOINT is retrieved from environment variables. Logging confirms the endpoint retrieval. Step 2: Authentication Managed Identity Authentication: The ManagedIdentityCredential class is used for secure Azure authentication. The SubscriptionClient is initialized to access Azure subscriptions. Retrieves a token for Azure Cognitive Services (https://cognitiveservices.azure.com/.default). Step 3: Flattening Dictionaries Function: flatten_dict Transforms nested dictionaries into a flat structure. Handles nested lists and dictionaries recursively. Used for preparing metadata for storage in CSV. Step 4: Resource Tag Filtering Functions: get_resources_by_tag_in_subscription: Filters resources in a subscription based on a tag key and value. get_resource_groups_by_tag_in_subscription: Identifies resource groups with matching tags. Purpose: Retrieve Azure resources and resource groups tagged with specific key-value pairs. Step 5: Resource Metadata Retrieval Functions: get_all_resources: Aggregates resources and resource groups across all accessible subscriptions. get_resources_in_resource_group_in_subscription: Retrieves resources from specific resource groups. get_latest_api_version: Determines the most recent API version for a given resource type. get_resource_metadata: Retrieves detailed metadata for individual resources using the latest API version. Purpose: Collect comprehensive resource details for further processing. Step 6: Documentation Generation Function: generate_infra_config Processes metadata through OpenAI to generate documentation. OpenAI generates detailed and human-readable descriptions for Azure resources. Multi-stage review process: Initial draft by OpenAI. Feedback loop with ArchitecturalReviewer and DocCreator for refinement. Final content is saved to architecture.txt. Step 7: Workload Overview Function: generate_workload_overview Reads from the generated CSV file to create a summary of the workload. Sends resource list to OpenAI for generating a high-level overview. Step 8: Conversion to DOCX Function: txt_to_docx Creates a Word document (Output.docx) with: Section 1: "Workload Overview" (generated summary). Section 2: "Workload Details" (detailed resource metadata). Adds structured headings and page breaks. Step 9: Temporary Files Cleanup Function: cleanup_files Deletes temporary files: architecture.txt resources_with_expanded_metadata.csv Output.docx Ensures no residual files remain after execution. Step 10: CSV Metadata Export Function: save_resources_with_expanded_metadata_to_csv Aggregates and flattens resource metadata. Saves details to resources_with_expanded_metadata.csv. Includes unique keys derived from all metadata fields. Step 11: Architectural Review Process Functions: ArchitecturalReviewer: Reviews and suggests improvements to documentation. DocCreator: Incorporates reviewer suggestions into the documentation. Purpose: Iterative refinement for high-quality documentation. Step 12: HTTP Trigger Function Function: smartdocs Accepts HTTP requests with tag_key and tag_value parameters. Orchestrates the entire workflow: Resource discovery. Metadata retrieval. Documentation generation. File cleanup. Responds with success or error messages. Components Azure OpenAI service is a platform provided by Microsoft that offers access to powerful language models developed by OpenAI, including GPT-4, GPT-4o, GPT-4o mini, and others. The service is used in this scenario for all the natural language understanding and generating communication to the customers. Azure Functions is a serverless solution that allows you to write less code, maintain less infrastructure, and save on costs. Instead of worrying about deploying and maintaining servers, the cloud infrastructure provides all the up-to-date resources needed to keep your applications running. Azure Function App Managed Identities allow to authenticate to any resource that supports Microsoft Entra authentication, including your own applications. Azure libraries for Python (SDK) are the open-source Azure libraries for Python designed to simplify the provisioning, management and utilisation of Azure resources from Python application code. Potential use cases The Azure OpenAI Smart Doc Creator Function App, like all proposed solutions, can be modified to suit your needs. It can be of practical help when there is a need to obtain all the configurations, in terms of metadata, of the resources and services that make up a workload. Contributors Principal author: Tommaso Sacco | Cloud Solutions Architect Simone Verza | Cloud Solution Architect Extended Contribution: Saverio Lorenzini | Senior Cloud Solution Architect Andrea De Gregorio | Technical Specialist Gianluca De Rossi | Technical Specialist Special Thanks: Carmelo Ferrara | Director CSA Marco Crippa | Sr CSA Manager
Availability Zone Resiliency on Ecommerce Reference Application
The Resilient Ecommerce Reference Application is a synthetic workload that mirrors a simple, bare-bones, e-commerce platform. The purpose of it is to demonstrate how to use Azure Resiliency best practices to achieve availability during zonal outages or components outages.Building scalable and persistent AI applications with LangChain, Instaclustr, and Azure NetApp Files
Discover the powerful combination of LangChain and LangGraph for building stateful AI applications and unlock the benefits of using a managed-database service like NetApp® Instaclustr® backed by Azure NetApp Files for seamless data persistence and scalability.
Example Reference Network Topologies for API Management in private mode.
Learn about suggested Network example topologies thought to follow Vnet integration practices within the API management. Therefore, these revolve around API management in internal mode. API Management in internal mode requires a Premium SKU or Development (not recommended for production).AI Studio End-to-End Baseline Reference Implementation
Discover the Future of AI Deployment with Azure AI Studio’s Baseline Reference Implementation Azure AI Studio is reshaping the landscape of cloud AI integration with its commitment to operational excellence and strategic alignment with core business objectives. We are thrilled to introduce Azure AI Studio’s end-to-end baseline reference implementation—a streamlined architecture crafted for seamless, scalable, and secure AI cloud deployments. Embark on a journey to deploy sophisticated AI workloads with confidence, supported by Azure AI Studio's robust baseline architecture. Whether it's hosting interactive AI playgrounds, constructing complex AI workflows with Promptflow, or ensuring resilient and secure deployments within Azure's managed network environment, this implementation is your blueprint for success. Embrace a new era of AI innovation where security and scalability converge with organizational compliance and governance. Join us in deploying tomorrow's AI solutions, today.
