Category: Microsoft Azure

MS Ignite

Microsoft Ignite 2023: A Brief Summary

In the Microsoft Ignite 2023 event, Microsoft outlines how AI transformation is reshaping work and how Microsoft is supporting customers, partners and developers with its AI solutions. It covers:

  • Rethinking cloud infrastructure with new AI optimized silicon, Azure Boost, and partnerships with AMD and NVIDIA.
  • Extending the Microsoft Copilot experience across Microsoft 365, Copilot Studio, Copilot for Service, Copilot in Microsoft Dynamics 365 Guides, and Bing Chat and Bing Chat Enterprise.
  • Bringing Copilot to everyone with the general availability of Bing Chat and Bing Chat Enterprise as Copilot.
  • Reinforcing the data and AI connection with Microsoft Fabric, a unified platform for data management and AI tools, and integration with Microsoft Office and Teams.
  • Unlocking more value for developers with Azure AI Model-as-a-Service, Azure AI Studio, Vector Search, and new GPT models.
  • Enabling the responsible deployment of AI with the Copilot Copyright Commitment, Azure AI Content Safety, and new AI and productivity tools for developers.
  • Introducing new experiences in Windows to empower employees, IT and developers with Windows AI Studio and NVIDIA AI foundry service.
  • Strengthening defenses in the era of AI with the Unified Security Operations Platform and Security Copilot embedded within Microsoft Defender XDR and other Microsoft security products.

Page Reference – Microsoft Ignite 2023: AI transformation and the technology driving change – The Official Microsoft Blog

November 16, 2023 Artificial Intelligence (AI), Artificial Intelligence (AI), Microsoft 365, Microsoft Azure No comments yet
Azure's September Updates - What's New

Azure’s September Updates – What’s New

Azure’s September Updates: Overview

This blog post provides Azure’s September Updates and brief information about each update.

Azure Microsoft has released several major feature updates in September 2023. These updates enhance the performance, security, and usability of Azure services. Here are some of the highlights:

Azure’s September Updates: Synapse Analytics

This update introduces a new query engine that supports both SQL and Spark workloads. The query engine optimizes the execution of complex queries across multiple data sources. It also enables real-time streaming analytics and machine learning integration.

Azure Active Directory

This update adds support for passwordless authentication using biometrics, FIDO2 devices, or phone sign-in. Using passwordless authentication helps to lower the chances of falling victim to phishing attempts or having your login credentials stolen. It also improves the user experience and productivity.

Azure Kubernetes Service

This update enables automatic scaling of node pools based on the workload demand. It also supports Windows Server containers and Azure Arc integration. These features allow users to run hybrid and multi-cloud applications on Azure Kubernetes Service.

Azure Cognitive Services

This update enhances the capabilities of several cognitive services, such as Computer Vision, Speech, and Language Understanding. The update adds new features such as object detection, sentiment analysis, and entity linking. It also improves the accuracy and performance of existing features.

Azure’s September Updates: DevOps

This update introduces a new dashboard that provides a comprehensive view of the development process. The dashboard shows the status of code commits, builds, tests, deployments, and feedback. It also allows users to customize the dashboard with widgets and charts.

These are some of the major feature updates that Azure Microsoft has released in September 2023. For more details, please visit the official Azure blog.

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October 6, 2023 Azure - What's New, Microsoft Azure No comments yet
Azure Malware Scanning: A Comprehensive Guide

Azure Malware Scanning: A Comprehensive Guide

Are you looking for a way to protect your Azure storage accounts from malware threats? Do you want to ensure that your data is safe and secure in the cloud? If yes, then you need to know about Azure Malware Scanning in Defender for Storage.

Azure Malware Scanning is a feature that scans your Azure Blob storage accounts for malware infections. It detects and alerts you of any malicious files that may compromise your data or applications. You can also use it to quarantine or delete the infected files automatically.

This blog post will explain Azure Malware Scanning, its benefits, and usage.

What is Azure Malware Scanning in Defender for Storage?

Azure Malware Scanning is a feature that leverages Microsoft’s threat intelligence and machine learning to scan your Azure Blob storage accounts for malware. It supports both block blobs and append blobs, and scans both new and existing files.

Azure Malware Scanning is part of Azure Defender for Storage, a security service providing advanced threat protection for your Azure storage accounts. Azure Defender for Storage also offers anomaly detection, encryption enforcement, firewall auditing, and more.

What are the advantages of Azure Malware Scanning in Defender for Storage?

Malware Scanning in Defender for Storage offers several advantages for your cloud security, such as:

  • Helps you prevent data breaches and comply with regulatory standards by detecting and removing malware from your storage accounts.
  • Saves you time and resources by scanning your files automatically and continuously without requiring any manual intervention or configuration.
  • Gives you visibility and control over your storage security by providing you with alerts, reports, and remediation options.
  • Integrates with other Azure services and tools, such as Azure Security Center, Azure Sentinel, Azure Monitor, and Microsoft 365 Defender.

How to use?

To use Malware Scanning in Defender for Storage, you need to follow these steps:

  1. Enable Azure Defender for Storage on your subscription or resource group level. You can do this from the Azure portal, PowerShell, or CLI.
  2. Configure the malware scan settings for your storage accounts. You can choose to scan all or selected containers and specify the action to take when malware is detected. You can quarantine, delete, or log the infected files.
  3. Monitor the scan results and alerts from the Azure portal, Security Center, Sentinel, or Monitor. You can also view the scan reports and statistics from the Defender dashboard.
  4. Review and remediate the infected files from the quarantine container or the log file. If you’ve accidentally deleted some files, don’t worry! You can still retrieve them using the soft delete feature.

That’s it! You have successfully enabled and used Malware Scanning in Defender for Storage. Now, you can enjoy a more secure and reliable cloud storage experience.

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October 6, 2023 Artificial Intelligence (AI), Azure - What's New, Microsoft Azure No comments yet
Azure and Llama 2: A Powerful Combination

Azure and Llama 2: A Powerful Combination

Are you looking for a cloud platform that offers high performance, scalability, security, and flexibility? If so, you should consider Azure and Llama 2, the latest version of the popular open-source framework for building cloud-native applications. In this blog post, we will explore the features and benefits of Azure and Llama 2 and show you how to get started with them.

What is Azure?

Azure is Microsoft’s cloud computing platform that provides a range of services and solutions for various scenarios, such as web hosting, data analytics, artificial intelligence, Internet of Things, and more. Azure has over 200 products and services that you can use to build, deploy, and manage your applications on the cloud. Some of the advantages of Azure are:

  • Supports multiple languages, frameworks, and tools, such as .NET, Java, Python, Node.js, Visual Studio, GitHub, etc.
  • Offers global coverage with more than 60 regions and 170+ data centers worldwide.
  • The pay-as-you-go pricing model lets you only pay for what you use, with no upfront costs or termination fees.
  • Built-in security features and compliance standards protect your data and applications from threats and breaches.
  • A rich ecosystem of partners and third-party integrations that enhance its capabilities and functionality.

What is Llama 2?

Llama 2 is the second major release of Llama, an open-source framework for building cloud-native applications using microservices architecture. Llama 2 aims to simplify the development, deployment, and management of microservices on the cloud. Some of the features of Llama 2 are:

  • Supports multiple programming languages, such as Java, Kotlin, Scala, Groovy, etc.
  • Provides a set of libraries and tools that help you create, test, and run your microservices, such as Llama Boot, Llama Cloud, Llama CLI, etc.
  • Enables you to use various cloud services and platforms, such as Azure, AWS, Google Cloud Platform, Kubernetes, Docker, etc.
  • Offers a reactive and non-blocking approach that improves the performance and scalability of your applications.

How do you use Azure and Llama 2 together?

Using Azure and Llama 2 together can help you leverage the best of both worlds: the power and flexibility of Azure’s cloud services and the simplicity and productivity of Llama’s microservices framework. Here are some steps to get started with Azure and Llama 2:

  1. Create an Azure account if you don’t have one already. You can get a free trial with $200 credit for 12 months.
  2. Install Llama CLI on your local machine. You can download it from the official website or use a package manager such as Homebrew or Chocolatey.
  3. Create a new Llama project using the command `llama init`. You can choose from various templates and options to suit your needs.
  4. Add Azure dependencies to your project using the command `llama add azure`. This will enable you to use Azure’s services such as App Service, Cosmos DB, Service Bus, etc.
  5. Deploy your project to Azure using the command `llama deploy azure`. This will create or update the necessary resources on Azure and upload your application code.
  6. Enjoy your cloud-native application running on Azure!

Conclusion

Azure and Llama 2 are a powerful combination for cloud computing that can help you build modern, scalable, secure, and flexible applications on the cloud. You can use them together to create microservices-based applications that take advantage of Azure’s rich features and services.

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September 21, 2023 Artificial Intelligence (AI), Microsoft Azure No comments yet
Azure Static Website Hosting Made Easy

Azure Static Website Hosting Made Easy

This blog post will teach us how to host a static website in Azure Storage. A static website consists of HTML, CSS, JavaScript, and image files that do not require any server-side processing. Azure Storage is a scalable and cost-effective service that allows you to store and access data from anywhere.

Overview

Azure Storage offers a feature called static website hosting, which enables you to serve your static website directly from a storage account. You do not need to create or manage any web servers or virtual machines. You only need to upload your website files to a designated container in your storage account and configure a few settings.

Features of Azure Storage for Azure Static Website

Some of the benefits of using Azure Storage for Azure static website hosting are:

  • Low cost: You only pay for the storage space and bandwidth you use. There are no additional charges for web servers or other resources.
  • High availability: Azure Storage provides 99.9% availability for read operations and 99.99% for write operations. Your website will be accessible even if one or more regions experience an outage.
  • Scalability: Azure Storage can handle any amount of traffic and data. You can easily scale up or down your storage account as your needs change.
  • Security: Azure Storage supports encryption at rest and in transit. You can also use Azure Active Directory (AAD) to control access to your storage account and website files.
  • Performance: Azure Storage integrates with Azure Content Delivery Network (CDN), which caches your website files at edge locations worldwide. This reduces latency and improves user experience.

Getting Started with Azure Static Website

To host a static website in Azure Storage, you need to follow these steps:

  1. Create an Azure Storage account or use an existing one. Ensure that the account is of the general-purpose v2 (GPv2) type and supports HTTPS traffic.
  2. Enable static website hosting on your storage account. Specify the container’s name that will store your website files and the name of the default document (usually index.html).
  3. Upload your website files to the container using any tool or method that supports Azure Blob storage, such as Azure Portal, Azure CLI, or Visual Studio Code.
  4. Enable Azure CDN on your storage account and create a CDN endpoint for it. This will generate a URL that you can use to access your website.
  5. Map your custom domain name to the CDN endpoint using your DNS provider. You can also enable HTTPS on your custom domain using a free certificate from Azure CDN.

Conclusion

Hosting a static website in Azure Storage is a simple and cost-effective solution that offers high availability, scalability, security, and performance. You can easily deploy and update your website without worrying about managing any web servers or virtual machines. You can also leverage Azure CDN to optimize your website delivery and user experience.

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September 21, 2023 Microsoft Azure No comments yet
Azure AI Services: Innovate with Cognitive Power

Azure AI Services: Innovate with Cognitive Power

Introduction

In today’s fast-paced digital world, the power of artificial intelligence (AI) is indispensable for creating user-centric applications. Microsoft Azure offers a suite of AI-driven services under Azure Cognitive Services; each designed to enhance user experiences and streamline operations. Let’s delve into these nine Azure Cognitive Services to understand how they can revolutionize your applications.

Azure AI Services Related Blog Posts

  • Enhance User Engagement with Azure Personalizer: Azure Personalizer enables real-time content recommendations and personalization, elevating user experiences. Analyzing user behavior tailors content and recommendations to keep users engaged and satisfied.
  • Spatial Analysis with Azure Cognitive Services: Azure Cognitive Services can turn physical spaces into intelligent environments. Through spatial analysis, you can gather data to make informed decisions and create smarter, data-driven spaces.
  • Azure Translator: Bridging Language Gaps: Azure Translator breaks language barriers by offering automatic translation services. Your content can now reach a global audience, ensuring inclusivity and expanding your application’s reach.
  • Azure Face API: Recognizing Faces with Precision: Azure Face API provides facial recognition and identification capabilities, enhancing application security and personalization. It can also be employed for user authentication and access control.
  • Azure Speech Services: Immersive Voice Experiences: Azure Speech Services brings voice recognition and synthesis to your applications, delivering immersive experiences. Users can interact naturally with your apps through voice commands and responses.
  • Azure Computer Vision: Insights from Images: Azure Computer Vision extracts valuable insights from images, enabling data-driven decision-making. It identifies objects, text, and even emotions, making it invaluable for various industries.
  • Azure Text Analytics: Understand Customer Sentiment: Azure Text Analytics performs sentiment analysis on customer feedback, helping you understand customer satisfaction and pain points. This data can drive improvements and boost customer relations.
  • Azure Form Recognizer: Streamline Document Workflows: Azure Form Recognizer automates document processing, reducing manual data entry and streamlining workflows. From invoices to forms, it extracts valuable information accurately.
  • Language Understanding with LUIS: Language Understanding with LUIS (Language Understanding Intelligent Service) empowers applications to comprehend user intent and context, making them smarter and more user-friendly.

Conclusion

By harnessing the capabilities of Azure Cognitive Services, you can unlock the potential of AI to create more personalized, efficient, and user-centric applications. From real-time personalization to spatial analysis and language translation, Azure Cognitive Services offers a comprehensive toolkit for developers to elevate their applications in today’s AI-driven world.

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September 7, 2023 Artificial Intelligence (AI), Microsoft Azure No comments yet
Azure Digital Twins: Features and Advantages

Azure Digital Twins: Features and Advantages

Overview

Azure Digital Twins is a platform that enables you to create digital representations of physical environments and assets. You can use it to model complex scenarios, monitor real-time data, and optimize performance and efficiency. In this blog post, we will explore some of the features and benefits of Azure Digital Twins, and how you can get started with it.

Features of Azure Digital Twins

  • Spatial Intelligence Graph: This is the core component of Azure Digital Twins. It allows you to define the relationships and interactions between people, places, and devices in your digital twin. You can use predefined models or create your own custom ones.
  • Live Execution Environment: This is where you can run your digital twin logic and queries. You can use Azure Functions, Logic Apps, or custom code to implement your business logic and workflows. You can also use Azure Stream Analytics, Azure Synapse Analytics, or Power BI to analyze and visualize your data.
  • Integration with Azure IoT Hub: You can connect your physical devices and sensors to Azure IoT Hub, and then map them to your digital twin entities. This way, you can stream real-time data from your devices to your digital twin, and vice versa.
  • Integration with other Azure services: You can leverage other Azure services to enhance your digital twin solutions. For example, you can use Azure Maps to add geospatial context, Azure Cognitive Services to add AI capabilities, or Azure Security Center to secure your digital twin.

Advantages of Azure Digital Twins

  • Scalability: Handle large-scale, complex scenarios with millions of entities and relationships. You can scale up or down as needed and pay only for what you use.
  • Flexibility: You can model any scenario and use any data source. You can use the built-in models or create your custom ones. You can also use any programming language and framework to develop your digital twin logic and queries.
  • Interoperability: Supports open standards and protocols, such as DTDL (Digital Twins Definition Language), OPC UA (Open Platform Communications Unified Architecture), and MQTT (Message Queuing Telemetry Transport). Easily integrate with other platforms and systems on-premises and in the cloud.
  • Innovation: Enables you to create new and innovative solutions for various domains and industries, such as smart buildings, smart cities, smart manufacturing, smart healthcare, and more. You can simulate scenarios, optimize outcomes, and generate insights that were not possible before.

Getting Started

To get started, you need to follow these steps:

  1. Create an Azure account and an Azure Digital Twins instance.
  2. Define your digital twin model using DTDL or the built-in models.
  3. Upload your model to your Azure Digital Twins instance using the Azure portal or the SDKs.
  4. Connect your devices and sensors to Azure IoT Hub and map them to your digital twin entities.
  5. Implement your digital twin logic and queries using Azure Functions, Logic Apps, or custom code.
  6. Analyze and visualize your data using Azure Stream Analytics, Azure Synapse Analytics, or Power BI.

Conclusion

Azure Digital Twins is a powerful platform that allows you to create digital representations of physical environments and assets. You can use it to model complex scenarios, monitor real-time data, and optimize performance and efficiency. You can also integrate with other Azure services to add more capabilities and value to your digital twin solutions. To learn more, visit the official documentation page here.

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September 7, 2023 Internet of Things (IOT), Microsoft Azure No comments yet
Azure Sphere: IoT Protection Made Simple

Azure Sphere: IoT Protection Made Simple

Introduction

If you want to create, connect, and maintain secured intelligent IoT devices from the edge to the cloud, you might want to check out Azure Sphere. It is a secured, high-level application platform with built-in communication and security features for internet-connected devices. This blog post will explore what Azure Sphere offers, its advantages, and the steps to use it.

What is Azure Sphere?

Azure Sphere is a product of Microsoft that consists of three components:

  • Secured, connected, crossover microcontroller unit (MCU) that integrates real-time processing capabilities with the ability to run a high-level operating system.
  • Custom high-level Linux-based operating system (OS) that provides a secured application environment, authenticated connections, and over-the-air updates.
  • Cloud-based security service that provides continuous, renewable security for the device, data, and infrastructure. It also enables interoperation with IoT platform services like Azure IoT Hub and IoT Central.

The Sphere MCUs can be embedded into new devices or used as guardian modules to connect existing devices to the cloud. Sphere devices can be updated, controlled, monitored, and maintained remotely through the Azure Sphere Security Service.

What are the advantages?

The Sphere offers several benefits for IoT developers and users, such as:

  • Protects your device, data, and infrastructure on all fronts—hardware, software, and in the cloud. It implements the seven properties of highly secured devices identified by Microsoft research. They are the hardware-based root of trust, small trusted computing base, certificate-based authentication, renewable security, defense in depth, compartmentalization, and failure reporting.
  • Simplifies device management and maintenance by providing automatic software updates from the cloud to any connected device. You can deploy updates and improvements to your application alongside your OS directly to the IoT device over-the-air (OTA).
  • Helps you focus on your business strategy and innovation by reducing the complexity and cost of developing secured IoT solutions. You can leverage flexible implementation options and bring-your-own-cloud connectivity to deploy your solutions faster.
  • Enables you to collect product usage data and customer feedback over a secured connection. You can use this data to diagnose problems, provide new functionality, and design better products.

How to use Azure Sphere?

To get started, you need to follow these steps:

  1. Order an Azure Sphere development kit from one of the hardware partners. The development kit includes an Azure Sphere MCU board and a USB cable.
  2. Install the Azure Sphere SDK on your Windows or Linux machine. The SDK includes tools and libraries for developing and debugging applications for Azure Sphere devices.
  3. Register your device with the Azure Sphere Security Service using the Azure Sphere CLI or Visual Studio Code extension. This will assign a unique ID to your device and enable it to receive OS updates and application deployments from the cloud.
  4. Develop your application using Visual Studio or Visual Studio Code to create your application for Sphere devices. You can use C or C++ as the programming language and leverage the Sphere libraries and APIs for communication and security features.
  5. Deploy your application using Visual Studio or Visual Studio Code to build and deploy your application to your device via USB or OTA. You can also use the Sphere CLI or REST API to manage your deployments programmatically.

Conclusion

To learn more, visit the official website or check out the documentation.

We hope this blog post has given you an overview of Azure Sphere, its advantages, and how to use it. If you have any questions or feedback, please leave a comment below.

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September 7, 2023 Internet of Things (IOT), Microsoft Azure No comments yet
Azure Knowledge Mining: A Powerful AI Solution

Azure Knowledge Mining: A Powerful AI Solution

Overview

Are you looking for a way to extract valuable insights from your data, regardless of its format, location, or complexity? Do you want to leverage the power of artificial intelligence (AI) to search, analyze, and explore your content at scale? If so, you might be interested in Azure Knowledge Mining, an emerging discipline in AI that uses a combination of intelligent services to quickly learn from vast amounts of information.

What is Azure Knowledge Mining?

According to Microsoft, knowledge mining is “an emerging discipline in artificial intelligence (AI) that uses a combination of intelligent services to quickly learn from vast amounts of information. It allows organizations to deeply understand and easily explore information, uncover hidden insights, and find relationships and patterns at scale.”

Azure Knowledge Mining is the Microsoft solution for knowledge mining, based on Azure Cognitive Search, the only cloud search service with built-in AI capabilities. Azure Cognitive Search enables you to ingest content from various sources, enrich it with AI skills such as natural language processing, computer vision, and machine learning, and explore it through search, bots, applications, and data visualizations.

What are the benefits?

Azure Knowledge Mining can help you gain faster insights from diverse content types, customize your solution for your industry needs, and enable knowledge extraction wherever your data lives. Some of the benefits of Azure Knowledge Mining are you can:

  • Ingest content from Azure sources like Azure Blob storage, Azure Table storage, Azure SQL Database, Azure Cosmos DB, and hundreds of third-party sources via dedicated connectors.
  • Extract text-based content from file formats such as PDF, Microsoft Word, PowerPoint, and CSV. See the full list of supported formats.
  • Enrich the content with AI skills to extract information, find patterns, and deepen understanding. For example, you can extract entities, key phrases, sentiments, locations, languages, images, audio, and more from your content.
  • Apply machine learning models as custom skills for specific requirements like industry-specific regulations or custom entity extraction.
  • Explore the newly indexed data via search, bots, existing business applications, and data visualizations. You can also use semantic search to understand user intent and contextually rank the most relevant search results for users.

How to use Azure Knowledge Mining?

To use Azure Knowledge Mining, you must follow three steps: ingest, enrich, and explore.

  1. Ingest: You need to create an Azure Cognitive Search service in the Azure portal and connect it to your data sources using indexers or push APIs. You can also use the Import data wizard in the portal to quickly create an index from your data source.
  2. Enrich: You must define a skillset specifying the AI skills you want to apply to your content. You can use predefined cognitive skills or custom skills that you create using Azure Machine Learning or other tools. You can also use the Knowledge Store feature to project enriched documents into tables or objects for further analysis.
  3. Explore: You need to create a search index that stores the enriched documents and enables fast and flexible querying. You can use the Search explorer in the portal to test your queries and see the results. You can also use the Azure Cognitive Search SDKs or REST APIs to integrate search functionality into your applications or services.

Conclusion

Azure Knowledge Mining is a powerful AI solution that can help you uncover latent insights from all your content. You can use Azure Cognitive Search and other Azure AI services to ingest, enrich, and explore your data at scale and deliver enhanced experiences to your users and customers.

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September 6, 2023 Artificial Intelligence (AI), Microsoft Azure No comments yet
Azure Confidential Computing: Key Benefits

Azure Confidential Computing: Key Benefits

Overview

Azure confidential computing comprises technologies that safeguard your data and models at every stage of the AI lifecycle, even when in use. This allows you to execute AI workloads with sensitive data without risking unauthorized access or tampering. In this blog post, we’ll explore what Azure confidential computing offers, its advantages, and how you can employ it to develop secure AI solutions.

What is Azure Confidential Computing?

Azure confidential computing is grounded in the concept of trusted execution environments (TEEs). TEEs are hardware-protected memory areas that isolate code and data from the rest of the system. They thwart access or modification by anyone, including cloud operators, malicious admins, or privileged software like the hypervisor. TEEs also offer cryptographic attestation, validating the integrity and identity of the code within.

It supports two TEE types: software-based and hardware-based. Software-based TEEs use techniques like encryption and sandboxing, creating isolated environments. Hardware-based TEEs utilize dedicated hardware features like secure enclaves or protected memory, ensuring more robust isolation. Azure provides both TEE types through various services and VM sizes.

Advantages Confidential Computing

It provides several advantages for AI developers and users:

  • Protecting data and models in use: Run AI workloads with sensitive data (e.g., personal, financial, or health information) without exposing them to unauthorized access or tampering. Safeguard model architecture and weights from theft or reverse-engineering.
  • Enabling new scenarios and collaborations: Unlock new possibilities for AI applications demanding high security and privacy. Enable multi-party training and federated learning without sharing data or models centrally.
  • Increasing trust and compliance: Boost trust and transparency in your AI solutions by offering verifiable proof of data and model protection. Comply with regulations such as GDPR or HIPAA mandating data privacy and protection.

How to Utilize Azure Confidential Computing for AI?

The Confidential Computing offers multiple services and tools for building AI solutions with TEEs. Here are some examples:

  • Azure Machine Learning: Train and deploy AI models using hardware-based TEEs (e.g., Intel SGX or AMD SEV). Orchestrate federated learning across edge devices or cloud nodes.
  • Azure Cognitive Services: Access pre-built AI models for vision, speech, language, and decision-making using software-based TEEs (e.g., Open Enclave SDK or Intel SGX). Customize these models securely with your data.
  • NVIDIA GPU VMs: Run GPU-accelerated AI workloads using hardware-based TEEs (e.g., NVIDIA A100 Tensor Core GPUs with Ampere Protected Memory). Ensure data and model confidentiality and integrity while harnessing GPU performance.
  • Microsoft Research Confidential AI: Explore cutting-edge research projects and tools that delve into the confidential computing frontier for AI. Examples include CrypTFlow2 for secure multi-party computation on encrypted data and CryptoNets for encrypted model inference.

Conclusion

Azure confidential computing empowers you to safeguard your data and models throughout the AI lifecycle, even during use. With Azure confidential computing, you can create trustworthy AI solutions that deliver security, privacy, collaboration, and compliance benefits. To delve deeper into Azure confidential computing and get started, click here.

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September 6, 2023 Artificial Intelligence (AI), Microsoft Azure No comments yet
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