Kategorie: Microsoft Azure

Exploring open-source capabilities in Azure AI

This post was co-authored by Richard Tso, Director of Product Marketing, Azure AI

Open-source technologies have had a profound impact on the world of AI and machine learning, enabling developers, data scientists, and organizations to collaborate, innovate, and build better AI solutions. As large AI models like GPT-3.5 and DALL-E become more prevalent, organizations are also exploring ways to leverage existing open-source models and tools without needing to put a tremendous amount of effort into building them from scratch. Microsoft Azure AI is leading this effort by working closely with GitHub and data science communities, and providing organizations with access to a rich set of open-source technologies for building and deploying cutting-edge AI solutions.

At Azure Open Source Day, we highlighted Microsoft’s commitment to open source and how to build intelligent apps faster and with more flexibility using the latest open-source technologies that are available in Azure AI.

Build and operationalize open-source State-of-the-Art models in Azure Machine Learning

Recent advancements in AI propelled the rise of large foundation models that are trained on a vast quantity of data and can be easily adapted to a wide variety of applications across various industries. This emerging trend provides a unique opportunity for enterprises to build and use foundation models in their deep learning workloads.

Today, we’re announcing the upcoming public preview of foundation models in Azure Machine Learning. It provides Azure Machine Learning with native capabilities that enable customers to build and operationalize open-source foundation models at scale. With these new capabilities, organizations will get access to curated environments and Azure AI Infrastructure without having to manually manage and optimize dependencies. Azure Machine learning professionals can easily start their data science tasks to fine-tune and deploy foundation models from multiple open-source repositories, starting from Hugging Face, using Azure Machine Learning components and pipelines. This service will provide you with a comprehensive repository of popular open-source models for multiple tasks like natural language processing, vision, and multi-modality through the Azure Machine Learning built in registry. Users can not only use these pre-trained models for deployment and inferencing directly, but they will also have the ability to fine-tune supported machine learning tasks using their own data and import any other models directly from the open-source repository.

The next generation of Azure Cognitive Services for Vision

Today, Azure Cognitive Services for Vision released its next generation of capabilities powered by the Florence large foundational model. This new Microsoft model delivers significant improvements to image captioning and groundbreaking customization capabilities with few-shot learning. Until today, model customization required large datasets with hundreds of images per label to achieve production quality for vision tasks. But, Florence is trained on billions of text-image pairs, allowing custom models to achieve high quality with just a few images. This lowers the hurdle for creating models that can fit challenging use cases where training data is limited.

Users can try the new capabilities of Vision underpinned by the Florence model through Vision Studio. This tool demonstrates a full set of prebuilt vision tasks, including automatic captioning, smart cropping, classifying images and a summarizing video with natural language, and much more. Users can also see how the tool helps track movements, analyze environments, and provide real-time alerts.

To learn more about the new Florence model in Azure Cognitive Services for Vision, please check out this announcement blog.

New Responsible AI Toolbox additions

Responsible AI is a critical consideration for organizations building and deploying AI solutions. Last year, Microsoft launched the Responsible AI Dashboard within the Responsible AI Toolkit, a suite of tools for a customized, responsible AI experience with unique and complementary functionalities available on GitHub and in Azure Machine Learning. We recently announced the addition of two new open-source tools designed to make the adoption of responsible AI practices more practical.

The Responsible AI Mitigations Library allows practitioners to experiment with different mitigation techniques more easily, while the Responsible AI Tracker uses visualizations to demonstrate the effectiveness of different mitigations for more informed decision-making. The new mitigations library bolsters mitigation by offering a means of managing failures that occur in data preprocessing. The library complements the toolbox’s Fairlearn fairness assessment tool, which focuses on mitigations applied during training time. The tracker allows practitioners to look at performance for subsets of data across iterations of a model to help them determine the most appropriate model for deployment. When used with other tools in the Responsible AI Toolbox, they offer a more efficient and effective means to help improve the performance of systems across users and conditions. These tools are made open source on GitHub and integrated into Azure Machine Learning.

Accelerate large-scale AI with Azure AI infrastructure

Azure AI Infrastructure provides massive scale-up and scale-out capabilities for the most advanced AI workloads in the world. This is a key factor as to why leading AI companies, including our partners at OpenAI continue to choose Azure to advance their AI innovation on Azure AI. Our results for training OpenAI's GPT-3 on Azure AI Infrastructure using Azure NDm A100 v4 virtual machines with NVIDIA’s open-source framework, NVIDIA NeMo Megatron, delivered a 530B-parameter benchmark on 175 virtual machines, resulting in a scalability factor of 95 percent. When Azure AI infrastructure is used together with a managed end-to-end machine learning platform, such as Azure Machine Learning, it provides the vast compute needed to enable organizations to streamline management and orchestration of large AI models and help bring them into production.

The full benchmarking report for GPT-3 models with the NVIDIA NeMo Megatron framework on Azure AI infrastructure is available here.

Optimized training framework to accelerate PyTorch model development

Azure is a preferred platform for widely used open-source framework—PyTorch. At Microsoft Ignite, we launched Azure Container for PyTorch (ACPT) within Azure Machine Learning, bringing together the latest PyTorch version with our best optimization software for training and inferencing, such as DeepSpeed and ONNX Runtime, all tested and optimized for Azure. All these components are already installed in ACPT and validated to reduce setup costs and accelerate training time for large deep learning workloads. ACPT curated environment allows our customers to efficiently train PyTorch models. The optimization libraries like ONNX Runtime and DeepSpeed composed within the container can increase production speed up from 54 percent to 163 percent over regular PyTorch workloads as seen on various Hugging Face models.

The chart shows ACPT that combines ONNX Runtime and DeepSpeed can increase production speed up to 54 percent to 163 percent over regular PyTorch workloads.

This month, we’re bringing a new capability to ACPT—Nebula. Nebula is a component in ACPT that can help data scientists to boost checkpoint savings time faster than existing solutions for distributed large-scale model training jobs with PyTorch. Nebula is fully compatible with different distributed PyTorch training strategies, including PyTorch Lightning, DeepSpeed, and more. In saving medium-sized Hugging Face GPT2-XL checkpoints (20.6 GB), Nebula achieved a 96.9 percent reduction in single checkpointing time. The speed gain of saving checkpoints can still increase with model size and GPU numbers. Our results show that, with Nebula, saving a checkpoint with a size of 97GB in a training job on 128 A100 Nvidia GPUs can be reduced from 20 minutes to 1 second. With the ability to reduce checkpoint times from hours to seconds—a potential reduction of 95 percent to 99.9 percent, Nebula provides a solution to frequent saving and reduction of end-to-end training time in large-scale training jobs.

The chart shows Nebula achieved a 96.9 percent reduction in single checkpointing time with GPT2-XL.

To learn more about Azure Container for PyTorch, please check out this announcement blog.

MLflow 2.0 and Azure Machine Learning

MLflow is an open-source platform for the complete machine learning lifecycle, from experimentation to deployment. Being one of the MLflow contributors, Azure Machine Learning made its workspaces MLflow-compatible, which means organizations can use Azure Machine Learning workspaces in the same way that they use an MLflow tracking server. MLflow has recently released its new version, MLflow 2.0, which incorporates a refresh of the core platform APIs based on extensive feedback from MLflow users and customers, which simplifies the platform experience for data science and machine learning operations workflows. We’re excited to announce that MLflow 2.0 is also supported in Azure Machine Learning workspaces.

Read this blog to learn more about what you can do with MLflow 2.0 in Azure Machine Learning.

Azure AI is empowering developers and organizations to build cutting-edge AI solutions with its rich set of open-source technologies. From leveraging pre-trained models to customizing AI capabilities with new technologies like Hugging Face foundation models, to integrating responsible AI practices with new open-source tools, Azure AI is driving innovation and efficiency in the AI industry. With Azure AI infrastructure, organizations can accelerate their large-scale AI workloads and achieve even greater results. Read this blog and the on-demand session to take a deep dive into what open-source projects and features we’ve announced at Azure Open Source Day 2023.

We’d like to conclude this blog post with some outstanding customer examples that demonstrate their success strategy of combining open-source technologies and building their own AI solutions to transform businesses.

What is most important about these announcements is the creative and transformative ways our customers are leveraging open-source technologies to build their own AI solutions.

These are just a few examples from our customers.

Customers innovating with open-source on Azure AI

Elekta is a company that provides technology, software, and services for cancer treatment providers and researchers. Elekta considers AI as essential to expanding the use and availability of radiotherapy treatments. AI technology helps accelerate the overall treatment planning process and monitors patient movement in real-time during treatment. Elekta uses Azure cloud infrastructure for the storage and compute resources needed for their AI-enabled solutions. Elekta relies heavily on Azure Machine Learning, Azure Virtual Machines, and the PyTorch open-source machine learning framework to create virtual machines and optimize their neural networks.
Read full story

The National Basketball Association (NBA) is using AI and open-source technologies to enhance its fan experience. The NBA and Microsoft have partnered to create a direct-to-consumer platform that offers more personalized and engaging content to fans. The NBA uses AI-driven data analysis system, NBA CourtOptix, which uses player tracking and spatial position information to derive insights into the games. The system is powered by Microsoft Azure, including Azure Data Lake Storage, Azure Machine Learning, MLflow, and Delta Lake, among others. The goal is to turn the vast amounts of data into actionable insights that fans can understand and engage with. The NBA also hopes to strengthen its direct relationship with fans and increase engagement through increased personalization of content delivery and marketing efforts.
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AXA, a leading car insurance company in the United Kingdom needed to streamline the management of its online quotes to keep up with the fast-paced digital marketplace. With 30 million car insurance quotes processed daily, the company sought to find a solution to speed up deployment of new pricing models. In 2020, the AXA data science team discovered managed endpoints in Azure Machine Learning and adopted the technology during private preview. The team tested ONNX open-source models deployed through managed endpoints and achieved a great reduction in response time. The company intends to use Azure Machine Learning to deliver value, relevance, and personalization to customers and establish a more efficient and agile process.
Read full story

Quelle: Azure

Azure WAF guided investigation Notebook using Microsoft Sentinel for automated false positive tuning

With special thanks to Pete Bryan, Principal Security Research Manager, Microsoft Security.

The SQL injection attack remains one of the critical attacks in the OWASP Top 10, and it involves injecting a SQL query via the input data field into a web application without input validation. According to Microsoft Digital Defense Report 2022, 67 percent of web application exploits include SQL injections.

Azure Web Application Firewall (Azure WAF) provides centralized protection of your web applications from exploits and vulnerabilities. It protects against OWASP Top 10 attacks, bot attacks, application layer Distributed Denial of Service (DDoS) attacks, and other web attacks.

Azure WAF detects SQL injection attacks and blocks them by default. In certain instances, this could be a false positive that requires investigation and creation of Azure WAF exclusions. To complete a successful investigation, full context about the attack is needed and a process that guides you through the investigation is required.

We are pleased to announce a new Azure WAF guided investigation to tune WAF policy Notebook in preview. It guides you through an investigation experience to understand the Azure WAF incidents in Microsoft Sentinel, identify false positives, and automatically apply exclusions to WAF rules to address the false positives. This Notebook allows you to understand the WAF alert and pivot on key entities of the WAF event such as the request URI, client IP, hostname, and correlate with Threat Intelligence feeds to get a holistic view of the attack surface.

Azure WAF investigations powered by Microsoft Sentinel

Azure WAF is deeply integrated with Microsoft Sentinel, Microsoft’s Security Information and Event Management (SIEM) solution. Using the existing Azure WAF data connector, WAF logs are ingested and later analyzed for a variety of web application attacks and powerful visualizations pivoting on the full attack pattern are presented to you. This Notebook is built using Microsoft Threat Intelligence Center’s MSTICpy packages. With this Notebook, you can access rich historical contextual information using Microsoft Sentinel’s capabilities like incident generation, entity graph, and threat intelligence correlation, in conjunction with Azure WAF’s SQL injection detections based on OWASP rules and Microsoft Threat Intelligence rules.

Automated investigation and mitigation of web application attacks

Our new Azure WAF guided investigation to tune WAF policy Notebook provides an automated guided investigation for triaging Sentinel incidents triggered by Azure WAF SQL injection rules.

The solution includes the following components:

Azure WAF data connector in Microsoft Sentinel.
Microsoft Sentinel incidents that are generated due to SQL injection attack detected by the Microsoft Sentinel analytic rules.
Azure WAF Notebook that helps investigate Azure WAF logs and automatically applies WAF exclusions to the WAF policy.

A high-level diagram explaining the data flow is given below:

Let us look at two use case scenarios for using this Notebook:

Understanding the attack landscape when there is a true positive

Using the Notebook, you can pivot on various attack artifacts such as IP, URL, or domain threat intelligence, and understand the entity graph. This Notebook retrieves the WAF SQLi rule that generated the detection and looks up related SQLi rule events within the pre-selected time. Based on the above details, if you decide that the SQL injection attack is valid then you can update the incident severity and priority. In this scenario, the web application remains protected by Azure WAF.

Understand the attack pattern and create exclusions if there is a false positive

Using the Notebook, you can pivot on various attack artifacts such as IP, URL, or domain threat intelligence, and understand the entity graph. This Notebook retrieves the WAF SQLi rule that generated the detection and looks up related rule events. It also retrieves raw WAF logs to understand the relations between the request URI, client Ips, hostname entities and allows you to dynamically access the OWASP rule set in GitHub to understand the rule match pattern. Based on the investigations, if you decide this incident is a false positive, the process to automatically create granular exclusions is presented to you and the exclusions are applied to the Azure WAF policy using Azure WAF APIs.

The following personas would benefit from this Notebook:

Persona: Developer at SomeUnionFlight.com

Understanding SQL injection detection logic

Chris is a developer at SomeUnionFlight.com. His company hosts a website for users to search for flights and make flight reservations. They have hosted their website behind WAF with Azure Front Door (AFD) where AFD accepts user requests to search their website. SomeUnionFlight.com has an SQL backend where they store flight information. He notices that when users try to access the website, their access is getting blocked because the URL has “Union” keyword which is triggering the SQL injection rule. This detection is considered as a false positive because the “Union” keyword is used to mention a website name and not an SQL injection attack. He would like an investigation experience that helps him understand how to analyze this detection using Microsoft Sentinel and determine if it is a false positive. He would also like to automatically create exclusions for false positives for the URL without having to disable the entire rule.

Persona: SecOps analyst at Contoso.com

Understanding collateral attack vectors

Ashley is a Security Operations analyst at Contoso.com. Her company has purchased both Azure WAF and Microsoft Sentinel. She oversees analyzing WAF logs and identifying attack patterns. She would like to understand if the client IP or the request URI associated with the WAF rule that triggered the SQL injection are Indicators of Compromise (IoC). By understanding related Threat Intelligence Indicators of Compromises, she can prevent future attacks on her organization.

Get started today

SQL injection attacks are getting more prevalent by the day and Azure WAF protects web applications from these attacks. To enable a high-quality investigation experience for Azure WAF customers, we have created this new Azure WAF guided investigation Notebook that enables you quickly understand full attack surface and take actions on the incidents. You can follow our step-by-step instructions to learn how to use the Notebook.

This new Azure WAF Notebook can be found in Microsoft Sentinel under the Notebooks in the Threat Management section.

 
Quelle: Azure

Microsoft commercial marketplace: Spend smarter, move faster

Business-to-Business (B2B) commerce is experiencing a revolution driven largely by the mass adoption of cloud computing and the movement towards a simpler, more digital buying experiences. At the same time, cloud budgets are strong and mostly growing—IDC is predicting that cloud services will surpass USD1.7 trillion by 2026 while sustaining compound annual growth of almost 17 percent. But, with economic uncertainty, companies and organizations are challenged to “do more with less” and make the most of every dollar invested.

Cloud marketplaces offer you the ability to gain flexibility and control over your spending and make the most out of your cloud budget. This is why cloud marketplaces are expected to see a massive growth, from USD8 billion last year to USD50 billion by 20262 as predicted by Bessemer Venture Partners. To accelerate IT modernization and maximize the value of investments, we are innovating and improving Microsoft commercial marketplace to support your entire cloud portfolio.

The marketplace: Customized to your needs

There is often a misconception with cloud marketplaces that you lose touch with your vendors and are limited to “the sticker price.” We understand the complexities of enterprise deal making and the marketplace is a tool to help you simplify this process so you can focus on the innovation you need to deliver. While there is always the option to buy “off the shelf” solutions, we’re continuing to improve our custom dealmaking and private offer capabilities so you can spend smart and move fast.

With private offers, you can engage directly with partners, negotiate custom terms, pricing, and payment options, and get customized solutions delivered straight to your inbox. And with recent improvements in our marketplace, partners can create and deliver these private offers in less than 15 minutes—and eligible purchases can count towards your Microsoft Azure Consumption Commitment (MACC). We are the only cloud provider that by default applies the entire purchase* towards your commitment (and there are thousands). As more and more customers look to maximize the value of their investments, we’ve seen over 300 percent increase in customers with a MACC buying through marketplace.

Plus, the marketplace saves you the hassle of onboarding new vendors and streamlines the complexity of purchasing so it is all invoiced directly on your Azure bill. This enables you to simplify procurement and reduce legal complexity by purchasing applications under a standard contract. All solutions using the standard contract contain the same terms and conditions, so your legal team only needs to review once.  And, when you purchase solutions through marketplace, you are working with the most trusted cloud and our extensive set of best-in-breed capabilities across app innovation, data and AI, infrastructure, and security.

More and more of our customers—and sellers—are thinking marketplace first when getting their cloud solution. By being marketplace first, you can cut down on time to value and manage costs based on usage. Whether you are new to the Microsoft commercial marketplace or if you’ve already been using it to find solutions for your business, we are excited to help you along your journey. Taking advantage of the resources and tools will streamline the transaction process, maximize the value of your cloud spend, and you can buy with confidence knowing that these solutions are backed by Microsoft.

These enhancements are removing the frictions in B2B commerce, and simultaneously helping you to make the most of every cloud dollar spent. Here’s how you make the most of this opportunity:

Maximizing value with partner solutions that count towards consumption commitments

You can shop for Azure solutions that qualify towards your consumption commitment on the Azure portal. All you need to do is:

Log into Azure
Go to the marketplace tab
Select “Azure benefit eligible”

From there you can further refine search results based on your criteria. We have thousands of solutions available, so when you purchase these through marketplace—the amount you spend can count towards your cloud consumption commitment. Here are examples of a few providers with MACC-eligible solutions:

Fortinet offers a comprehensive security fabric platform to help industries accelerate security, maximize productivity, preserve user experience, and lower total cost of ownership.

Featured solution: Fortinet FortiGate Next-Generation Firewall (VM)

Empowering users to unlock the value of their data with Informatica’s Intelligent Data Management Cloud (IDMC) solution on Azure.

Featured solution: Informatica Intelligent Data Management Cloud

MongoDB Atlas, the industry’s first unified developer data platform, allows you to accelerate and simplify how you build with data on Azure modern applications.

Featured solution: MongoDB Atlas 

Break through your bad data and get the insight you need to consolidate, clean up, and maintain your data with Profisee’s solutions.

Featured solution: Profisee MDM—SaaS 

Conquer your analytics challenges, from experimental to mission critical, with faster decisions in the cloud.

Featured solution: SAS® Viya® portfolio on SAS® Cloud

Get agentless scanning with complete visibility and actionable context for your multi-cloud environment in minutes so your teams can proactively identify, prioritize, remediate, and prevent risks.

Featured solution: WIZ Cloud Infrastructure Security Platform

More and more of our customers—and sellers—are thinking “marketplace first” when getting their cloud solutions. By managing your cloud portfolio through marketplace, you can cut down on time to value and manage costs based on usage.

Whether you are new to the Microsoft commercial marketplace or if you’ve already been using it to find solutions for your business, we are excited to help you along your journey. Take advantage of the resources and tools within the marketplace to increase your efficiency, buy with confidence, and spend smarter.

Learn more about Microsoft commercial marketplace

Shop marketplace
Learn about Azure consumption commitments 
Get a private Azure marketplace

1IDC, Worldwide Whole Cloud Forecast, 2022–2026: The Next Stage of the Shift to a Cloud-Centric Technology Industry; US49857122, December 2022.

2Bessemer Venture Partners, 2022 State of the Cloud report.
Quelle: Azure

Microsoft commercial marketplace: empowering customized experiences to drive innovation

Business-to-Business (B2B) commerce is experiencing a revolution driven largely by the mass adoption of cloud computing and the movement towards a simpler, more digital buying experiences. At the same time, cloud budgets are strong and mostly growing—IDC is predicting that cloud services will surpass USD1.7 trillion by 2026 while sustaining compound annual growth of almost 17 percent. But, with economic uncertainty, companies and organizations are challenged to “do more with less” and make the most of every dollar invested.

Cloud marketplaces offer you the ability to gain flexibility and control over your spending and make the most out of your cloud budget. This is why cloud marketplaces are expected to see a massive growth, from USD8 billion last year to USD50 billion by 20262 as predicted by Bessemer Venture Partners. To accelerate IT modernization and maximize the value of investments, we are innovating and improving Microsoft commercial marketplace to support your entire cloud portfolio.

The Marketplace: Customized to your needs

There is often a misconception with cloud marketplaces that you lose touch with your vendors and are limited to “the sticker price.” We understand the complexities of enterprise deal making and the marketplace is a tool to help you simplify this process so you can focus on the innovation you need to deliver. While there is always the option to buy “off the shelf” solutions, we’re continuing to improve our custom dealmaking and private offer capabilities so you can spend smart and move fast.

With private offers, you can engage directly with partners, negotiate custom terms, pricing, and payment options, and get customized solutions delivered straight to your inbox. And with recent improvements in our marketplace, partners can create and deliver these private offers in less than 15 minutes—and eligible purchases can count towards your Microsoft Azure Consumption Commitment (MACC). We are the only cloud provider that by default applies the entire purchase* towards your commitment (and there are thousands). As more and more customers look to maximize the value of their investments, we’ve seen over 300 percent increase in customers with a MACC buying through marketplace.

Plus, the marketplace saves you the hassle of onboarding new vendors and streamlines the complexity of purchasing so it is all invoiced directly on your Azure bill. This enables you to simplify procurement and reduce legal complexity by purchasing applications under a standard contract. All solutions using the standard contract contain the same terms and conditions, so your legal team only needs to review once.  And, when you purchase solutions through marketplace, you are working with the most trusted cloud and our extensive set of best-in-breed capabilities across app innovation, data and AI, infrastructure, and security.

More and more of our customers—and sellers—are thinking marketplace first when getting their cloud solution. By being marketplace first, you can cut down on time to value and manage costs based on usage. Whether you are new to the Microsoft commercial marketplace or if you’ve already been using it to find solutions for your business, we are excited to help you along your journey. Taking advantage of the resources and tools will streamline the transaction process, maximize the value of your cloud spend, and you can buy with confidence knowing that these solutions are backed by Microsoft.

These enhancements are removing the frictions in B2B commerce, and simultaneously helping you to make the most of every cloud dollar spent. Here’s how you make the most of this opportunity:

Maximizing Value with Partner Solutions that Count Towards Consumption Commitments

You can shop for Azure solutions that qualify towards your consumption commitment on the Azure portal. All you need to do is:

Log into Azure
Go to the marketplace tab
Select “Azure benefit eligible”

From there you can further refine search results based on your criteria. We have thousands of solutions available, so when you purchase these through marketplace—the amount you spend can count towards your cloud consumption commitment. Here are examples of a few providers with MACC-eligible solutions:

Fortinet offers a comprehensive security fabric platform to help industries accelerate security, maximize productivity, preserve user experience, and lower total cost of ownership.

Featured solution: Fortinet FortiGate Next-Generation Firewall (VM)

Empowering users to unlock the value of their data with Informatica’s Intelligent Data Management Cloud (IDMC) solution on Azure.

Featured solution: Informatica Intelligent Data Management Cloud

MongoDB Atlas, the industry’s first unified developer data platform, allows you to accelerate and simplify how you build with data on Azure modern applications.

Featured solution: MongoDB Atlas 

Break through your bad data and get the insight you need to consolidate, clean up, and maintain your data with Profisee’s solutions.

Featured solution: Profisee MDM—SaaS 

Conquer your analytics challenges, from experimental to mission critical, with faster decisions in the cloud.

Featured solution: SAS® Viya® portfolio on SAS® Cloud

Get agentless scanning with complete visibility and actionable context for your multi-cloud environment in minutes so your teams can proactively identify, prioritize, remediate, and prevent risks.

Featured solution: WIZ Cloud Infrastructure Security Platform

More and more of our customers—and sellers—are thinking “marketplace first” when getting their cloud solutions. By managing your cloud portfolio through marketplace, you can cut down on time to value and manage costs based on usage.

Whether you are new to the Microsoft commercial marketplace or if you’ve already been using it to find solutions for your business, we are excited to help you along your journey. Take advantage of the resources and tools within the marketplace to increase your efficiency, buy with confidence, and spend smarter.

Learn more about Microsoft commercial marketplace

Shop marketplace
Learn about Azure consumption commitments 
Get a private Azure marketplace

1IDC, Worldwide Whole Cloud Forecast, 2022–2026: The Next Stage of the Shift to a Cloud-Centric Technology Industry; US49857122, December 2022.

2Bessemer Venture Partners, 2022 State of the Cloud report.
Quelle: Azure

What's new in Azure Data & AI: Azure is the best place to build and run AI workloads

It’s not every day that you get to be a part of something being discussed at dinner tables around the world—and we’re hearing this from customers and partners too. They’re eager to reimagine how they do business, embracing generative AI to deliver the next generation of apps and experiences. New and inspiring use cases surface daily, as AI technologies and practitioner communities mature. It’s both an exciting and humbling time for our team.

That excitement and inspiration would not be possible without the purpose-built AI infrastructure that can support the successful deployment and scalability of AI systems for large models. The infrastructure must have the means to rapidly expand with enough parallel processing power, low latency, and interconnected GPUs to train and inference complex AI models. This is why AI innovation leaders like OpenAI and Meta, and other companies around the globe, make Azure the cornerstone of their AI infrastructure.

Bringing together purpose-built AI infrastructure and managed data and AI services into one environment streamlines management and automation, often reducing the complexity of building, training, and bringing AI models into production. For example, Azure Machine Learning helps accelerate time to value with industry-leading machine learning operations (MLOps), open-source interoperability, and integrated tools to support data privacy, security, and real-time data processing.

There’s much conversation to have regarding new frameworks and strategies to help define and derive business value from AI. I hope you’ll join us March 14th for a virtual conversation between Susan Etlinger, Director of Data & AI Marketing at Microsoft; Atefah “Atti” Riazi, Chief Information Officer of Hearst; and Maribel Lopez, founder of Lopez Research as they share their unique perspectives on how business leaders can help their organization realize tangible return on investment (ROI).

I’m thrilled to share some of the new features and improvements helping our customers achieve more with data and AI this month.

Data is the fuel that powers AI

Our Executive Vice President of Cloud and AI, Scott Guthrie, says that data is the fuel that powers AI technology. Peak performance requires clean, integrated, and accessible data. Cloud solutions that are purpose-built to run AI models at scale also require operational databases, analytics, and governance solutions that work together—by design. To this end, the Microsoft Intelligent Data Platform is helping industry leaders like T-Mobile, BP, and Amadeus move beyond the cost and complexity of data fragmentation to a modern data estate built for AI innovation. Leveraging the platform opens opportunities for organizations to spend less time on data integration, and more time on value creation.

Benefits of Azure to on-premises and multi-cloud environments

Customers’ data management needs can vary widely depending on where they are on their digital transformation journey. Azure Arc extends the Azure platform to customers on-premises and multi-cloud scenarios for a unified hybrid environment. For example, Microsoft is partnering with Taoyuan City to deliver Taiwan's first municipal hybrid cloud platform with Azure Arc, Azure Synapse, and Power BI. The result is a hybrid environment that unlocks data insights while delivering control across their entire data estate, plus significant time and resources savings.

Be sure to explore pay-as-you-go licensing for Azure Arc-enabled SQL to provide on-premises SQL Server customers with a cloud billing model to only pay for what they use on a consumption-based meter. This is a great option for customers with seasonal needs that do not require a full license. To help optimize SQL Server running on-premises and in multi-cloud environments, we also recently announced general availability of the SQL best practice assessment on Azure Arc-enabled SQL Server for customers to diagnose potential issues more easily.

Migrating Windows Server and SQL Server to the cloud

Justifying the move to cloud is a crucial decision for a business, and it could be difficult to know where to start. In December, we introduced Azure Migrate business case to help organizations remove the guess work in migration cost planning with data-driven calculations. With just a few clicks, Azure Migrate Business Case will help you build a business proposal to understand how Azure SQL can bring the most value to your business, including: on-premise vs. Azure total cost of ownership analysis, year-on- year cashflow analysis, and resource utilization insights to identify servers and workloads that are ideal for cloud. It also helps identify quick wins for migration and modernization including end of support Windows OS and SQL versions. To get started, visit Azure Migrate.

Introducing Adaptive Protection in Microsoft Purview

Announced this month in preview, Adaptive Protection in Microsoft Purview leverages Insider Risk Management machine learning to understand how people are interacting with data. It helps identify risky activities that may result in data security incidents, then automatically tailors Data Loss Prevention (DLP) controls based on the risk detected. With Adaptive Protection, DLP policies become dynamic, ensuring that the most effective policy—such as blocking data sharing—is applied only to high-risk users, while low-risk users can maintain their productivity. This empowers security operations teams to operate more efficiently. Sign up for a free trial.

Innovate with industry-leading AI

While machine learning becomes more mainstream across industries, there are many challenges like data governance, security, data compliance, and auditability that companies need to consider when building and deploying models. Our approach to MLOps incorporates people, processes, and platform to deliver continuous value with fewer resources for machine learning—one of the reasons Microsoft was recently recognized as a leader in the IDC MarketScape: Worldwide for MLOps Platforms. Microsoft was also recently recognized as a leader in the IDC MarketScape: Worldwide General-Purpose Computer Vision AI Software Platform Vendor Assessment. As unstructured data like images and videos become more ubiquitous in the digital era, Microsoft is well-positioned to help organizations of all sizes leverage the power of computer vision to improve productivity, efficiency, safety, sustainability, and inclusivity.

Evaluate machine learning models

Last month we announced the general availability of the Responsible AI dashboard within the Responsible AI Toolbox, a suite of popular tools to support responsible AI deployments. The capabilities are open source on GitHub or can be accessed directly through the Azure Machine Learning platform. This month, we’ve launched a blog tutorial series exploring how to use the responsible AI dashboard and guide you through the entire process.

Today, we’re excited to announce two key capabilities to the Responsible AI Toolbox, the Responsible AI mitigations library and the Responsible AI tracker. First, the mitigations library helps machine learning practitioners experiment with different mitigation techniques more easily and manage common failures that may occur in data preprocessing. The library complements the toolbox’s Fairlearn fairness assessment tool, which focuses on mitigations applied during model training. Second, the responsible AI tracker uses visualizations to demonstrate the effectiveness of different mitigations for easier model comparison and validation. Practitioners can compare the performance of data subsets across model iterations to determine the most appropriate model for production. With these tools, organizations are better equipped to evaluate models to reduce biases and increase fairness.

Accelerate AI innovation in Azure Machine Learning

Machine learning requires secure collaboration among data scientists, machine learning engineers, IT admins, and developers. Organizations in regulated industries, like Belgium-based Isabel Group, rely on Azure Machine Learning to offer seamless integration through every step of the data pipeline. Microsoft continually improves product and business capabilities across the entire machine learning lifecycle to empower people of different skillsets. Below are some recent enhancements to help organizations get their models into production faster while ensuring enterprise-grade security and compliance in Azure Machine Learning:

Secure workspaces with network isolation for managed online endpoints (generally available): Offers the ability to secure the ingress and egress of managed online endpoints to help ensure compliance with enterprise security standards. Learn more about accessing storage services.

Compartmentalize access to machine learning data with identity passthrough (generally available): Uses role-based access controls (RBAC) to grant or deny users access to specific data. For example, when a training job in identity passthrough mode is submitted, Azure Machine Learning uses that identity to authenticate against data storage. Additionally, administrators can also restrict access for each user based on storage accounts, folders, or files and auditors can trace storage access requests back to specific users. Learn more about accessing storage services.

Train models using AutoML in Designer (preview): Employs an easy drag-and-drop ability to hook your data up to an AutoML task and deploy the best resulting model. An ideal solution for no-code/low-code users who want to build enterprise-quality machine learning models. Learn more about model training and deployment.

Skill up with Data & AI events and education

Build in-demand data and AI technical skills with Microsoft

Jumpstart your career or bolster your credentials with skilling resources from Microsoft. The new Cloud Skills Challenge for Azure Database Administrators offers free, interactive learning modules and the opportunity to engage with peers through a global community forum. For those with eyes on Azure certifications, the Microsoft Azure Connected Learning Experience (CLX) is designed to support exam readiness with on-demand resources and hands-on labs as well as cram sessions hosted by industry experts. Recently expanded tracks for Designing and Implementing a Microsoft Azure AI Solution and Administering Microsoft Azure SQL Solutions can help professionals build these in-demand skills.

For anyone interested in building apps with generative AI using Azure OpenAI Service, check out our new Introduction to Azure OpenAI Service learning course.

Join us for upcoming community events and skilling opportunities

I’d like to end my post by highlighting some of the upcoming events happening across Microsoft in March. These events are a great opportunity to meet other customers, partners, and technical experts across the Microsoft community to share stories and best practices. Please mark your calendars and join us.

Azure Open Source Day is a free on-demand virtual event on March 7th, bringing together developers, data scientists, and technical decision makers utilizing open source technologies. Presenters will explore open source projects like the Responsible AI Toolbox and collaborations with communities and technology partners like HuggingFace and NVIDIA. Register for Azure Open Source Day.

Microsoft Ability Summit is a free live digital event on March 8th where you can discover new accessible technologies and explore best practices to help everyone achieve more. In the meantime, you can also learn 6 ways to improve accessibility with Azure AI in our recent blog. Register for Microsoft Ability Summit.

Cosmos DB Conf is a free virtual developer event on March 28th co-organized by Microsoft and the Azure Cosmos DB community. Come and learn more about Azure Cosmos DB and see what others in the community are building. Register for Cosmo DB Conf.

Learn more about Azure Data and AI

Thanks so much for reading about the recent updates across our data and AI business over the last month. We have a lot of exciting things brewing and I can’t wait to share more product and service enhancements, customer stories, and upcoming news next month.
Quelle: Azure