FOCUS: A new specification for cloud cost transparency

When it comes to FinOps, the data is of the upmost importance. Data is the key to understanding your cloud cost and usage patterns, and pivotal to making smart decisions about your cloud strategy and operations. This is why Microsoft is proud to be a founding member of the FinOps Open Cost and Usage Specification (FOCUS) project and why we’re excited to add support in Cost Management after FOCUS 1.0 is available later this year. In the meantime, start preparing for FOCUS by familiarizing yourself with the current specification, joining the FOCUS community, and providing feedback on your use cases and needs.

What is FOCUS?

FOCUS is a groundbreaking initiative to define a common format for billing data that empowers organizations to better understand cost and usage patterns and optimize spending and performance across multiple cloud, SaaS, and even on-premises service offerings.

FOCUS will provide organizations with a consistent, clear, and accessible view of their cost data explicitly designed for FinOps needs such as allocation, analytics, monitoring, and optimization. As the new “language” of FinOps, FOCUS will enable practitioners to collaborate more efficiently and effectively with peers throughout the organization and even maximize transferability and onboarding for new team members, getting people up and running quicker. Paired with the FinOps Framework, practitioners will be armed with the tools needed to build a streamlined FinOps practice that maximizes the value of the cloud.

Why organizations need FOCUS

The variety and flexibility of Microsoft cloud services allows you to build amazing things while only paying for what you need, when you need it. And with this flexibility comes varying operational models where services are billed and can be tuned differently based on a variety of factors. When services are billed differently, their cost and usage data tends to differ as well, making it challenging to allocate, analyze, monitor, and optimize consistently. Of course, this goes beyond just Microsoft’s cloud services. Organizations often rely on software as a service (SaaS) products, licensed software, on-premises infrastructure, or even other clouds, exacerbating the problem with each provider sharing data in proprietary formats.

FOCUS solves this problem by establishing a provider and service agnostic data specification that addresses some of the biggest challenges organizations face in managing the value of their cloud investments—understanding and quantifying the business value of their spending. FOCUS will enable organizations to spend more time driving value and less struggling to understand data caused by inconsistencies between and unfamiliarity with different services and providers.

“At Walmart, we spend a lot of our time not only normalizing data across different clouds, but we’re also constantly reacting to changing SKUs and services in areas like Storage, Compute, and AI/ML. One of the most significant outcomes of FOCUS isn’t just that we’re aiming to simplify and standardize on a common specification, it’s the conversations that are starting on best practices – How should we all think about amortization for committed and reserved instances? What are our standard values for service categories?

It’s much more than just a conversation about a few fields. It’s a discussion that will help define best practices and standards for a cloud computing market that continues to expand into new areas like SaaS, IoT, and Gen AI. We’re discussing standards today that will be the foundation of how we talk about cost decades from now. It’s exciting.“—Tim O’Brien, Senior Director of Engineering, Cloud Cost Management at Walmart Global Tech.

Why Microsoft believes in FOCUS

But why would Microsoft want to join other cloud providers and SaaS vendors to promote a common billing data specification? Because consistent cloud billing promotes the innovation and experimentation that Azure is built to provide. Building and optimizing applications in Azure in an iterative way using modern architectures is easier when you clearly understand how you’re billed and can weigh cost equally amongst other business priorities in building those systems. Better collaboration between business, technical, and finance teams will make your organization more productive overall, which maps back to our core mission to empower every person and every organization on the planet to achieve more.

“At FinOps X 2022, when Udam Dewaraja first introduced the idea of the FinOps community and service providers joining forces to establish an open billing data specification, I was hooked but also somewhat skeptical about whether major cloud providers would be willing to engage and adopt this upcoming specification (and natively support the new dimensions and metrics). However, during the very first FOCUS meeting, Microsoft’s Cost Management team proved me wrong, and my skepticism quickly faded away!”—Irena Jurica, Solution Architect at CloudVane, Neos.

Widespread adoption of FOCUS will make allocating, analyzing, monitoring, and optimizing costs across providers as easy as using a single provider, enabling you to do more with less. FinOps skills become more portable than ever, and practitioners, vendors, and consultants will become more efficient and effective when moving to an organization that uses different clouds or SaaS products. Without having to spend time learning proprietary data formats, organizations can focus on value-added FinOps capabilities that help deliver real value.

Our adoption of FOCUS removes a barrier to cloud adoption and helps organizations make better data-driven decisions about their cloud use that translates to business value on top of the Microsoft cloud.

Getting started with FOCUS

The FOCUS 0.5 release was announced in June 2023 and introduced a standardized way to describe fundamental concepts which apply to any provider.

Resources are identified by a ResourceId and ResourceName and organized into their respective ServiceName and ServiceCategory. ServiceCategory enables you to organize your costs into a top-level set of categories consistent across cloud providers, which makes it especially interesting. You can also see additional details, like the Region a resource was deployed to, the PublisherName of the company who developed the service, and the ProviderName of the cloud where the service was used.

All charges include a ChargeType to describe what kind of charge it is (such as usage or purchase), the ChargePeriodStart and ChargePeriodEnd dates the charge applied to, and the applicable BilledCost and AmortizedCost amounts. This is one big deviation from the current Cost Management experiences: instead of pulling cost from separate actual (billed) and amortized cost datasets, with FOCUS, you can query all your data at once, which should speed up processing times and reduce storage size for anyone currently exporting both datasets.

All charges have BillingPeriodStart and BillingPeriodEnd dates, a BillingAccountId and BillingAccountName that links to the scope at which your invoices are generated, a SubAccountId and SubAccountName that indicates the lower-level subscription account where resources are managed, and an InvoiceIssuerName that indicates what organization you receive invoices from (such as Microsoft or a Microsoft partner). For anyone using Microsoft Customer Agreement, you may notice that the BillingAccountId is linked to your billing profile, since that’s where the invoice is generated. This will be an important distinction for Microsoft Cloud customers, given the different terminology. Similarly, SubAccountId is linked to your subscription, which will be a new cross-cloud term to familiarize yourself with for cost allocation and chargeback needs.

Of course, reading about FOCUS isn’t as good as working with the data. If you’d like to give FOCUS a test run, you can download a FOCUS sample Power BI report as part of the FinOps toolkit, an open source collection of reusable solutions to help you jump start your FinOps efforts.

You can also connect this report to your own data through the Cost Management connector for Power BI or by deploying a FinOps hub data pipeline.

For those interested in the data, you can also explore a small sample dataset along with a few other open datasets that can be used as part of your own data ingestion and cleanup efforts.

And lastly, if you’re interested in converting your own data in FOCUS, you can also leverage either the Invoke-FinOpsSchemaTransform or ConvertTo-FinOpsSchema command from the FinOps toolkit PowerShell module. These commands allow you to convert small datasets to FOCUS using a familiar command line interface.

Looking forward to FOCUS 1.0

But this was only the beginning. We’re incredibly excited to be a part of the FinOps community and help lead the way forward as FOCUS nears the 1.0 milestone. The FOCUS 1.0 specification is being driven forward by squads of project members, working backwards from the perspective of FinOps practitioners’ use cases. Practitioners are defining the columns they need to perform consistent cost allocation, to manage commitment-based discounts effectively, define consistent unit cost metrics and key performance indicators (KPIs), and more. Squads are building out the specification based on their needs to define consistent usage, pricing, and cost metrics, as well as for consistent inclusion of credits, discounts, and prepaid cost elements.

FOCUS is an important step for our industry, and for the adoption of FinOps in organizations around the world. Microsoft is proud to serve on the FOCUS Steering Committee, and on the Governing Board and Technical Advisory Council of the FinOps Foundation. Join us to help make FOCUS a standard worldwide!

Source: microsoft.com

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The new AI imperative: Unlock repeatable value for your organization with LLMOps

Time and again, we have seen how AI helps companies accelerate what’s possible by streamlining operations, personalizing customer interactions, and bringing new products and experiences to market. The shifts in the last year around generative AI and foundation models are accelerating the adoption of AI within organizations as companies see what technologies like Azure OpenAI Service can do. They’ve also pointed out the need for new tools and processes, as well as a fundamental shift in how technical and non-technical teams should collaborate to manage their AI practices at scale.  

This shift is often referred to as LLMOps (large language model operations). Even before the term LLMOps came into use, Azure AI had many tools to support healthy LLMOps already, building on its foundations as an MLOps (machine learning operations) platform. But during our Build event last spring, we introduced a new capability in Azure AI called prompt flow, which sets a new bar for what LLMOps can look like, and last month we released the public preview of prompt flow’s code-first experience in the Azure AI Software Development Kit, Command Line Interface, and VS Code extension.  

Today, we want to go into a little more detail about LLMOps generally, and LLMOps in Azure AI specifically. To share our learnings with the industry, we decided to launch this new blog series dedicated to LLMOps for foundation models, diving deeper into what it means for organizations around the globe. The series will examine what makes generative AI so unique and how it can meet current business challenges, as well as how it drives new forms of collaboration between teams working to build the next generation of apps and services. The series will also ground organizations in responsible AI approaches and best practices, as well as data governance considerations as companies innovate now and towards the future.  

From MLOps to LLMOps

While the latest foundation model is often the headline conversation, there are a lot of intricacies involved in building systems that use LLMs: selecting just the right models, designing architecture, orchestrating prompts, embedding them into applications, checking them for groundedness, and monitoring them using responsible AI toolchains. For customers that had started on their MLOps journey already, they’ll see that the techniques used in MLOps pave the way for LLMOps.  

Unlike the traditional ML models which often have more predictable output, the LLMs can be non-deterministic, which forces us to adopt a different way to work with them. A data scientist today might be used to control the training and testing data, setting weights, using tools like the responsible AI dashboard in Azure Machine Learning to identify biases, and monitoring the model in production.  

Most of these techniques still apply to modern LLM-based systems, but you add to them: prompt engineering, evaluation, data grounding, vector search configuration, chunking, embedding, safety systems, and testing/evaluation become cornerstones of the best practices.  

Like MLOps, LLMOps is also more than technology or product adoption. It’s a confluence of the people engaged in the problem space, the process you use, and the products to implement them. Companies deploying LLMs to production often involve multidisciplinary teams across data science, user experience design, and engineering, and often include engagement from compliance or legal teams and subject matter experts. As the system grows, the team needs to be ready to think through often complex questions about topics such as how to deal with the variance you might see in model output, or how best to tackle a safety issue.

Overcoming LLM-Powered application development challenges

Creating an application system based around an LLM has three phases:

• Startup or initialization—During this phase, you select your business use case and often work to get a proof of concept up and running quickly. Selecting the user experience you want, the data you want to pull into the experience (e.g. through retrieval augmented generation), and answering the business questions about the impact you expect are part of this phase. In Azure AI, you might create an Azure AI Search index on data and use the user interface to add your data to a model like GPT 4 to create an endpoint to get started.
• Evaluation and Refinement—Once the Proof of Concept exists, the work turns to refinement—experimenting with different meta prompts, different ways to index the data, and different models are part of this phase. Using prompt flow you’d be able to create these flows and experiments, run the flow against sample data, evaluate the prompt’s performance, and iterate on the flow if necessary. Assess the flow’s performance by running it against a larger dataset, evaluate the prompt’s effectiveness, and refine it as needed. Proceed to the next stage if the results meet the desired criteria.
• Production—Once the system behaves as you expect in evaluation, you deploy it using your standard DevOps practices, and you’d use Azure AI to monitor its performance in a production environment, and gather usage data and feedback. This information is part of the set you then use to improve the flow and contribute to earlier stages for further iterations.

Microsoft is committed to continuously improving the reliability, privacy, security, inclusiveness, and accuracy of Azure. Our focus on identifying, quantifying, and mitigating potential generative AI harms is unwavering. With sophisticated natural language processing (NLP) content and code generation capabilities through (LLMs) like Llama 2 and GPT-4, we have designed custom mitigations to ensure responsible solutions. By mitigating potential issues before application production, we streamline LLMOps and help refine operational readiness plans.

As part of your responsible AI practices, it’s essential to monitor the results for biases, misleading or false information, and address data groundedness concerns throughout the process. The tools in Azure AI are designed to help, including prompt flow and Azure AI Content Safety, but much responsibility sits with the application developer and data science team.

By adopting a design-test-revise approach during production, you can strengthen your application and achieve better outcomes.

How Azure helps companies accelerate innovation 

Over the last decade, Microsoft has invested heavily in understanding the way people across organizations interact with developer and data scientist toolchains to build and create applications and models at scale. More recently, our work with customers and the work we ourselves have gone through to create our Copilots have taught us much and we have gained a better understanding of the model lifecycle and created tools in the Azure AI portfolio to help streamline the process for LLMOps.  

Pivotal to LLMOps is an orchestration layer that bridges user inputs with underlying models, ensuring precise, context-aware responses.  

A standout capability of LLMOps on Azure is the introduction of prompt flow. This facilitates unparalleled scalability and orchestration of LLMs, adeptly managing multiple prompt patterns with precision. It ensures robust version control, seamless continuous integration, and continuous delivery integration, as well as continuous monitoring of LLM assets. These attributes significantly enhance the reproducibility of LLM pipelines and foster collaboration among machine learning engineers, app developers, and prompt engineers. It helps developers achieve consistent experiment results and performance. 

In addition, data processing forms a crucial facet of LLMOps. Azure AI is engineered to seamlessly integrate with any data source and is optimized to work with Azure data sources, from vector indices such as Azure AI Search, as well as databases such as Microsoft Fabric, Azure Data Lake Storage Gen2, and Azure Blob Storage. This integration empowers developers with the ease of accessing data, which can be leveraged to augment the LLMs or fine-tune them to align with specific requirements. 

And while we talk a lot about the OpenAI frontier models like GPT-4 and DALL-E that run as Azure AI services, Azure AI also includes a robust model catalog of foundation models including Meta’s Llama 2, Falcon, and Stable Diffusion. By using pre-trained models through the model catalog, customers can reduce development time and computation costs to get started quickly and easily with minimal friction. The broad selection of models lets developers customize, evaluate, and deploy commercial applications confidently with Azure’s end-to-end built-in security and unequaled scalability. 

LLMOps now and future 

Microsoft offers a wealth of resources to support your success with Azure, including certification courses, tutorials, and training material. Our courses on application development, cloud migration, generative AI, and LLMOps are constantly expanding to meet the latest innovations in prompt engineering, fine-tuning, and LLM app development.  

But the innovation doesn’t stop there. Recently, Microsoft unveiled Vision Models in our Azure AI model catalog. With this, Azure’s already expansive catalog now includes a diverse array of curated models available to the community. Vision includes image classification, object segmentation, and object detection models, thoroughly evaluated across varying architectures and packaged with default hyperparameters ensuring solid performance right out of the box. 

Source: microsoft.com

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Prompts are key in 2023: Twenty-five tips to help you unlock the potential of generative AI

In the last few years, generative AI has seen exponential growth. Language models like GPT-3.5-Turbo and GPT-4 on Azure OpenAI Service can automate content generation and conversational experiences, making it easier and more efficient to communicate with customers and end users.

AI input prompts defined

A prompt, in the context of AI, particularly in large language models, refers to the input or instruction given by users to elicit a specific type of response. To get the most out of large language models like GPT-4, it’s imperative to craft prompts that yield effective results. The challenge lies in choosing the best combination of words, expressions, symbols, and structures to steer the model toward producing accurate and pertinent content.

Why prompts matter

Just like when communicating in real life, how you ask for what you want can limit—or expand—the type of information you receive.

Prompts help specify the user’s intent and expectation from AI, hence more precise prompts lead to more accurate and relevant results. They allow users to obtain a wide variety of responses, from answering questions, creating stories in the tone of your favorite author, generating poetry, and even performing code-related tasks.

Similar prompts can lead to varying responses based on the underlying model, its training data, or even subtle variations in how you phrase your request.

Following you’ll find prompt tips to help you create the kind of content you need, whether at work or play.

Get started! Twenty-five prompt tips for your best content creation to date

• Know what you want
• Clearly outline the problem or need you’re trying to address. For instance, “I need fresh ideas for a marketing campaign geared toward our latest app.”
• Start with a simple question
• Begin with a simple question to check the model’s understanding, then build into more complex queries.
• Ask open-ended questions
• Generative models work best when they’re free to roam. Instead of asking, “Should I use social media for marketing?” ask, “What are some innovative ways to grab attention across my social media platforms.” You can even specify the specific platform you plan to use (X, LinkedIn, etc.).
• Iterate and refine
• Use the feedback you get from initial prompts then build on it. Like an idea about a general content marketing strategy? Follow up with, “How can I implement a content marketing strategy for a tech product designed to monitor how happy my pet is while I’m away from home?”
• Provide context
• The more context you provide, the better AI can tailor its response to your unique situation. For example, “I just released an app to track sleep patterns and am looking for low-cost marketing strategies that appeal to businesses concerned with their employee’s well-being.”
• State your boundaries
• Mention any constraints (e.g., budget, timeline, resources) upfront. “What are marketing strategies for a new product that can be executed within a 5,000 USD budget and within a two-week period?”
• Go big (then small)
• Break down big questions into smaller ones for more actionable insights. Instead of asking “How can I improve my business?” ask “How can I increase online sales?” Then, followed up with “What social media platforms are best for advertising beanbags?”
• Opposites attract: Marry creative and analytical requests
• AIs can handle both creative brainstorming and analytical tasks. But divide and conquer for the best results. Brainstorm marketing strategies, then follow up with “What are the pros and cons of influencer marketing?”
• Strengths, Weaknesses, Opportunities, Threats (SWOT)
• A SWOT analysis can help get your business on the right path. Ask AI to perform a SWOT analysis using specifics about your business for immediately actionable items you can get started on ASAP.
• Ask for examples
• Grasping a concept is easier with an example on hand. Ask for an example then use it as a potential model. You’ll discover what does—and doesn’t—work for your specific case. For example, “Can you provide a case study of a successful influencer marketing campaign?”
• Specify the format
• Want answers in bullet points, a paragraph, or a list? Make mention of that and see your wishes take literal form.
• Define tech terms
• If there’s a term or acronym specific to your industry, use a prompt to define it, or ensure the model understands the context in which it’s being used.
• Rephrase for precision
• If the initial answer isn’t satisfactory, rephrase your question. Not only will you get your creative communicative juices, but you’ll also find that answers to your questions can prompt new directions in your thinking.
• In-depth versus short-form
• Want a summary in a single paragraph or a pages-long academic deep dive? Make mention of your preferences in your prompt.
• Use negative instructions for a positive effect
• If you know what you don’t want, specify that. E.g., “Provide marketing strategies excluding online advertisements.”
• Multiple answers
• Ask for multiple answers or perspectives to a single question for a more comprehensive and nuanced understanding of your topic.
• Request sources
• While most models don’t browse the web in real-time, asking it to base its answer on known sources up to its last training data can provide greater credibility.
• Limit bias
• Explicitly ask the model to give an unbiased answer or to consider multiple perspectives.
• Context is key
• Ask for compliance requirements for specific industries. “What are the current trends in generative AI?” “What compliance requirements should I keep in mind for healthcare related topics?
• Power in numbers
• Quantify whenever possible: Need numbers or percentages? Metrics, distances, or speed? Include this request in your prompt.
• Avoid leading questions
• Ensure your question doesn’t steer the model towards a specific answer—unless that’s your intention.
• Tone it down (or up)
• If you need fun, out-of-the-box content versus a more academic tone, mention that. E.g., “Provide a fun creative tagline for a green energy campaign.”
• Provide real-world implications
• Explain why you need the answer or how it’ll be used for more context. We’re launching a new cookie testing app next month. How should we position it against competitor x?”
• Safety and accuracy
• Cross-reference critical information provided by the model with trusted external sources, especially if decisions based on the model’s answer have significant business implications.
• Refine over time
• If you’re using the model regularly, note down what types of prompts give you the best results and refine them accordingly.

Think of generative AI prompts as a multifunctional tool built for the digital age, adept at both enhancing business strategies and enriching our home lives. 

The goal is to use generative AI as a tool in your broader decision-making and brainstorming process. Combining AI’s suggestions with your expertise and knowledge of your business and market will yield the best results. By integrating these AI insights, we’re not merely keeping up with the times; we’re pioneering a future where efficiency meets innovation.

Our commitment to responsible AI

With Responsible AI tools in Azure, Microsoft is empowering organizations to build the next generation of AI apps safely and responsibly. Microsoft has announced the general availability of Azure AI Content Safety, a state-of-the art AI system that helps organizations keep AI-generated content safe and create better online experiences for everyone. Customers—from startup to enterprise – are applying the capabilities of Azure AI Content Safety to social media, education and employee engagement scenarios to help construct AI systems that operationalize fairness, privacy, security, and other responsible AI principles.

Source: microsoft.com

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The Microsoft Azure Incubations Team launches Radius, a new open application platform for the cloud

The Microsoft Azure Incubations team is excited to announce Radius, a cloud-native application platform that enables developers and platform engineers who support them to collaborate on delivering and managing cloud-native applications that follow corporate best practices for cost, operations, and security by default. Radius is an open-source project that supports deploying applications across private cloud, Microsoft Azure, and Amazon Web Services, with more cloud providers to come. 

Microsoft innovating via open source software

Microsoft is a major contributor to open-source projects across the industry and its Azure Incubations team is focused specifically on open-source innovation that enables everyone to accelerate their journey to the cloud. In addition to Radius, the team has launched multiple popular open source projects including Dapr, KEDA, and Copacetic, all available at github.com via the Cloud Native Compute Foundation (CNCF). 

The evolution of cloud computing has increased the speed of innovation for many companies, whether they are building second and third-tier applications or complex microservice-based applications. Cloud native technologies like Kubernetes have made building applications that can run anywhere easier. At the same time, many applications have become more complex, and managing them in the cloud is increasingly difficult, as companies build cloud-native applications composed of interconnected services and deploy them to multiple public clouds and their private infrastructure. While Kubernetes is a key enabler, we see many customers building abstractions over Kubernetes, usually focused on compute, to work around its limitations: Kubernetes has no formal definition of an application, it mingles infrastructure and application concepts and it is overwhelmingly complex. Developers also inevitably realize their applications require much more than Kubernetes, including support for dependencies like application programming interface (API) front ends, key-value stores, caches, and observability systems. Amidst these challenges for developers, their corporate IT counterparts also must enforce an ever-growing matrix of corporate standards, compliance, and security requirements, while still enabling rapid application innovation. 

Introducing Radius

Radius was designed to address these distinct but related challenges that arise across development and operations as companies continue their journey to the cloud. Radius meets application teams where they are by supporting proven technologies like Kubernetes, existing infrastructure tools including Terraform and Bicep, and by integrating with existing continuous integration and continuous delivery (CI/CD) systems like GitHub Actions. Radius supports multi-tier web-plus-data to complex microservice applications like eShop a popular cloud reference application from Microsoft.

Radius enables developers to understand their applications and it knows your application is more than just Kubernetes. Radius helps developers see all the components that comprise their application, and when they add new components, Radius automatically connects those components to their application by taking care of permissions, connection strings, and more.

Radius also ensures the cloud infrastructure used by applications meets cost, operations, and security requirements. These requirements are captured in recipes, which are defined by the IT operators, platform engineers, and/or security engineers that support cloud native developers. Radius binds an application to its dependent infrastructure, which enables Radius to provide an application graph that shows precisely how the application and infrastructure are interconnected. This graph enables team members to view and intuitively understand what makes up an application.

Many enterprises are multi-cloud and want solutions that work well not on just Azure, but on other clouds, as well as on-premises. So, Radius is open-source and multi-cloud from the start. Companies like Microsoft, BlackRock, Comcast, and Millenium BCP have worked together to ensure applications defined and managed with Radius can run on any cloud. Anyone in the open-source community can contribute to Radius, ensuring Radius evolves along with the broader cloud native community. Initial observations from these companies include:

“In today’s landscape of ever-evolving cloud complexities, there’s an imperative need to streamline the application development lifecycle. It’s essential that our internal developers can rapidly access the infrastructure they require, all while adhering to compliance standards and requirements. We see Radius as a promising enabler in this context. Through its unique offering of Radius recipes, the platform empowers developers to tap into vital cloud resources like Kubernetes and storage solutions, without the necessity to grasp the intricate details of these underlying systems. Our engagement with Radius stems from our advocacy for open-source solutions within our own technology platform, Aladdin, and we believe this approach holds significant potential to resonate with the cloud-native community.“ Mike Bowen, Senior Principal Engineer and OSPO Director, BlackRock.

“Radius is strongly aligned with our platform engineering vision to enable Comcast engineers to innovate at the speed of thought. We are prototyping on Radius to understand how Comcast might both consume and contribute to this promising open-source project.” Paul Roach, VP of Developer Experience, Comcast 

“At Millennium bcp our focus on security, compliance, best practices, and agility is paramount, and we must ensure these requirements are continuously met. To align expectations and lifecycles across multiple teams and technologies we are working to make common Application definitions and lifecycles first-class citizens in our IT landscape, while abstracting custom internal IT patterns and service contracts. We find this same vision in Radius. Our infrastructure can be handled exclusively by internal infra product teams, exposing only the Recipe to our developers to abstract complexity and ensure design decisions are made by the right people. Developers can focus on identifying what is relevant for their Applications, leveraging the correct Recipes without having to go into implementation concerns. This common contract correctly refocuses teams: developers focus exclusively on evolving the Application while infrastructure teams now manage infrastructure with a clear understanding of Application dependencies.” Nuno Guedes, Cloud Compute Lead, Millennium BCP

With Dapr, the Microsoft Azure Incubations Team helped developers write microservices with best practices, abstraction, portability, and separation from infrastructure. Now, we are doing the same for defining an application’s architecture. The two technologies strongly complement each other: Radius works with Dapr, simplifying Dapr configuration. Together, they enable, not just portable code, but portable applications.

Source: microsoft.com

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Azure Secrets Revealed: Boosting Business with Cloud Magic

In the digital age, businesses are constantly seeking innovative solutions to stay ahead of the curve. The advent of cloud computing has revolutionized the way organizations operate, with Microsoft Azure emerging as a game-changer. In this article, we will unlock the secrets of Azure, revealing how it can elevate your business to new heights.

Azure: Unveiling the Power of the Cloud

What Is Azure?

Azure is Microsoft's cloud computing platform, designed to provide a wide range of services for building, deploying, and managing applications through Microsoft-managed data centers. It offers an extensive suite of services, including virtual machines, databases, AI, IoT, and more.

Scalability Beyond Imagination

One of the secrets of Azure's success is its incredible scalability. Whether you're a startup or an enterprise, Azure adapts to your needs. You can scale up or down effortlessly, ensuring that your resources are always aligned with your business requirements. This elasticity is a true game-changer, allowing you to pay only for what you use.

Unparalleled Reliability

Azure's global network of data centers guarantees unmatched reliability. With multiple data centers worldwide, your data is safe, and your applications are always available. This is essential for businesses that cannot afford downtime.

Cutting-Edge Security

Security is a paramount concern in the digital world, and Azure takes it seriously. With Azure, your data is protected by a fortress of security measures, including advanced threat detection and encryption. Azure's security protocols are second to none.

Transforming Business with Azure

Streamlined Operations

Azure streamlines your business operations by providing you with tools and services that make management and deployment a breeze. From DevOps to automation, Azure simplifies complex processes, allowing you to focus on what matters most—your business.

Data-Driven Decisions

Data is the lifeblood of the modern enterprise. Azure offers a range of data services, including Azure SQL Database, Azure Cosmos DB, and Azure Synapse Analytics, that allow you to collect, store, and analyze data effectively. This data-driven approach empowers you to make informed decisions, driving your business forward.

AI and IoT Integration

Azure isn't just about infrastructure; it's about innovation. It offers robust AI and IoT capabilities, enabling you to build smart, connected solutions. Harness the power of Azure to create intelligent applications that learn and adapt, opening new doors for your business.

Azure Case Studies: Real-World Success

Toyota: Driving Innovation with Azure

Toyota, the world-renowned automotive manufacturer, embraced Azure for its cloud needs. By doing so, they optimized their production processes, improved supply chain management, and enhanced customer experiences. Azure became their secret weapon for innovation.

Johnson & Johnson: Healing with Azure

The healthcare giant, Johnson & Johnson, turned to Azure to accelerate drug discovery. By leveraging Azure's advanced data analytics capabilities, they significantly reduced research time, making the world a healthier place.

How to Get Started with Azure

Step 1: Assess Your Needs

Before diving into Azure, it's essential to assess your business needs. Consider your current infrastructure, goals, and budget. Azure offers a variety of pricing models to suit different scenarios.

Step 2: Create an Azure Account

To get started, you'll need to create an Azure account. You can choose from various subscription plans, including a free trial with a credit of $200 to explore Azure's services.

Step 3: Explore Azure Services

Once you have your account, start exploring Azure's services. Familiarize yourself with its vast catalog and choose the ones that align with your business goals.

Step 4: Implement Azure Solutions

Whether you need to migrate existing applications, build new ones, or enhance your business with AI, Azure has the tools to make it happen. Leverage the expertise of Azure professionals to ensure a seamless transition.

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Unlocking the Potential of Azure OpenAI Service: Success Stories

In a world dominated by technology, businesses are in a constant race to stay ahead of the curve. Leveraging cutting-edge tools and services has become crucial for staying competitive and meeting the evolving needs of customers. One such game-changing technology that has been transforming the landscape is the Azure OpenAI Service. In this article, we explore the immense potential of this service and share some remarkable success stories that demonstrate how it can take your business to new heights.

The Power of Azure OpenAI Service

What is Azure OpenAI Service?

Azure OpenAI Service is a sophisticated AI platform developed by Microsoft in collaboration with OpenAI. This powerful tool harnesses the capabilities of artificial intelligence, machine learning, and natural language processing to help businesses streamline their operations, improve customer experiences, and unlock new opportunities.

Versatile Applications

One of the standout features of Azure OpenAI Service is its versatility. It can be seamlessly integrated into various industry verticals, including e-commerce, healthcare, finance, and more. This adaptability makes it a valuable asset for businesses of all sizes and types.

Enhanced Productivity

By automating tasks, analyzing data, and providing valuable insights, Azure OpenAI Service empowers organizations to enhance productivity. It frees up human resources from routine and time-consuming tasks, allowing them to focus on strategic activities.

Improved Customer Experiences

In today's competitive market, customer experience is paramount. Azure OpenAI Service enables businesses to provide personalized, efficient, and 24/7 customer support through chatbots and virtual assistants. This not only delights customers but also drives brand loyalty.

Real Success Stories

To truly understand the potential of Azure OpenAI Service, let's delve into some real success stories from businesses that have harnessed its capabilities to transform their operations.

Success Story 1: E-Commerce Reinvented

An online retail giant was struggling to keep up with customer inquiries and support requests. They implemented Azure OpenAI Service to develop a chatbot that could provide instant responses to customer queries. The result? Customer satisfaction soared as response times were reduced, leading to a significant increase in sales.

Success Story 2: Healthcare Revolutionized

A leading healthcare provider was facing challenges in managing medical records and appointment scheduling efficiently. By integrating Azure OpenAI Service, they developed a virtual assistant that could handle appointment bookings and provide patients with medical information. This not only streamlined operations but also improved patient experiences.

Success Story 3: Financial Insights

A financial institution was drowning in data, making it challenging to extract valuable insights. Azure OpenAI Service came to the rescue, offering data analysis and reporting capabilities. This allowed the institution to make data-driven decisions, reduce risks, and enhance their financial services.

Implementing Azure OpenAI Service for Your Business

Now that you've seen the transformational power of Azure OpenAI Service through these success stories, you might be wondering how to implement it for your business. Here are the steps to get started:

1. Assess Your Needs: Identify the specific areas in your business operations where AI can make a difference. Whether it's customer support, data analysis, or automation, pinpoint your requirements.

2. Choose the Right Plan: Azure OpenAI Service offers various pricing and service plans. Select the one that aligns with your business needs and budget.

3. Integration: Work with experts to seamlessly integrate the service into your existing systems and processes.

4. Training and Optimization: Train your AI models for the best performance and continuously optimize them to adapt to changing business needs.

5. Monitor and Evaluate: Regularly monitor the performance of your AI solutions and gather feedback from users to make necessary improvements.

The Future of Business

As we move further into the digital age, the role of AI in business will only continue to expand. Azure OpenAI Service is at the forefront of this technological revolution, offering innovative solutions to everyday challenges. By embracing this service, businesses can stay competitive, enhance productivity, and provide exceptional customer experiences.

Unlocking the potential of Azure OpenAI Service is not just a choice; it's a necessity for those who aim to thrive in the dynamic world of modern business.

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Why Do You Need the AZ-900 Practice Test?

The Azure AZ-900 certification, a key offering in Microsoft's suite of cloud computing services, is steadily increasing its presence in the market. In fact, it stands out as one of the rare tools with the potential to challenge the dominant position of AWS in this domain. This article will emphasize how the AZ-900 practice test will lead to success.

The AZ-900 serves as the initial Azure certification, guaranteeing that you possess fundamental understanding of cloud computing and the capacity to maximize its capabilities.

If you're seeking insights into the exam content, the recommended preparation duration, and the types of questions you'll encounter, this article caters to your needs. Here, we'll provide comprehensive guidance for preparing for the exam, significantly increasing your likelihood of success by utilizing AZ-900 practice tests.

About the AZ-900 Exam

This exam itself lasts for 60 mins.

The exam consists of 40-60 questions in total.

To pass, you must obtain 700 points out of the 1000 points on the exam. In other words, you must pass 70% of the exam.

AZ-900 exam is available in English, Japanese, Chinese (Simplified), Korean, Spanish, German, French, Indonesian (Indonesia), Arabic (Saudi Arabia), Chinese (Traditional), Italian, Portuguese (Brazil), and Russian languages.

Microsoft Azure Fundamentals AZ-900 certification cost is USD 99.

The percentage-wise break up of skills measured by the module is as below:

Describe cloud concepts (25–30%)

Describe Azure architecture and services (35–40%)

Describe Azure management and governance (30–35%)

Target Audience

The examination targets individuals taking their initial strides in the realm of cloud computing, along with its associated services. It also caters to professionals who are novices in the domain of Azure.

This certification is ideal for showcasing your fundamental understanding of cloud computing and Microsoft Azure.

You can initiate this exam without prior knowledge, but ideally, you should possess expertise and background in IT, Data Science, and Software Development.

How to Study for the Microsoft AZ-900 Exam?

To ensure success, a strategic approach to preparation is essential. Let's delve into seven crucial tips to aid your effective and efficient preparation for the AZ-900 exam.

1. Understand the AZ-900 Syllabus and Exam Structure

Before diving into intensive preparation, it is imperative to comprehend the structure and syllabus of the AZ-900 exam thoroughly. Familiarize yourself with the exam pattern, question formats, and essential topics. You can get the complete details of the Microsoft Azure Fundamentals exam structure and syllabus from the official webpage or here.

2. Leverage Comprehensive Study Resources

Access various study resources such as official Microsoft documentation, online courses, practice tests, and reputable study guides to build a strong foundation. Use interactive learning tools to grasp complex concepts and reinforce your understanding of Azure services, pricing, and support.

3. Hands-On Practice with Azure Services

Theory alone cannot guarantee success. To truly comprehend Azure functionalities, engaging in hands-on practice with the various Azure services is crucial. Utilize the Azure free account to explore and experiment with different benefits, enabling you to gain practical insights and better understand their functionalities and use cases.

4. Join Study Groups and Forums

Engaging in discussions and study groups with like-minded individuals can significantly enhance your understanding and retention of key concepts. Platforms such as Microsoft's official community forums, Reddit, and other online discussion groups can provide valuable insights, tips, and strategies for tackling the AZ-900 exam effectively.

5. Develop a Customized Study Plan

Creating a well-structured study plan tailored to your learning style and schedule is crucial. Divide your study sessions into manageable segments, focusing on daily exam topics. Allocate ample time for revision and practice tests, ensuring a comprehensive understanding of each domain and its subtopics.

6. Review and Analyze Practice Tests

Regularly assess your progress and knowledge by taking practice tests. Analyze your performance, identify areas of improvement, and focus on strengthening your weak points. Utilize the insights gained from practice tests to fine-tune your study plan and concentrate on specific topics that require additional attention.

7. Stay Calm and Confident on Exam Day

Maintaining a calm and composed mindset on the exam day is essential. Ensure you have a good night's sleep before the exam, and avoid cramming new concepts at the last minute. Arrive at the exam center early, equipped with all the necessary documents and a positive mindset. Trust in your preparation and approach the exam with confidence.

10 Benefits of Utilizing AZ-900 Practice Test

Benefit 1: Acquire a Comprehensive Understanding of Exam Structure and Question Types

AZ-900 practice tests emulate the actual exam environment, enabling you to familiarize yourself with the structure of the test and the types of questions you may encounter. You'll build confidence and refine your exam-taking strategies by gaining exposure to various question formats, including multiple-choice, drag-and-drop, and case studies.

Benefit 2: AZ-900 Practice Test Helps in Identifying Knowledge Gaps and Weak Areas

Engaging with AZ-900 practice tests helps you identify your knowledge gaps and weak areas early in your preparation journey. By reviewing your performance in practice tests, you can pinpoint the topics that require additional focus and allocate your study time efficiently to reinforce your understanding of these critical concepts.

Benefit 3: AZ-900 Practice Test Boosts Confidence and Reduces Exam Anxiety

Regularly practicing with AZ-900 mock exams instills confidence and alleviates exam-related anxiety. As you become more familiar with the exam content and format, your test-taking apprehensions diminish, allowing you to approach the AZ-900 exam with a composed and focused mindset.

Benefit 4: AZ-900 Practice Test Enhances Time Management Skills

Efficient time management is vital during the AZ-900 exam. Practice tests are a valuable tool for honing your time management skills, enabling you to gauge the time required for each question and section. By practicing under timed conditions, you'll develop a strategic approach to tackling the exam within the stipulated timeframe.

Benefit 5: AZ-900 Practice Test Reinforces Retention of Key Concepts

Repeatedly engaging with AZ-900 practice tests reinforces the retention of critical concepts and Azure fundamentals. Consistent exposure to exam-related content solidifies your understanding of essential Azure services, pricing models, security measures. Moreover it pertinent topics, enhancing your overall retention and recall abilities.

Benefit 6: Familiarize Yourself with Real-world Scenarios and Case Studies

AZ-900 practice tests often incorporate real-world scenarios and case studies, providing practical insights into how Azure services are utilized in different business contexts. By encountering these scenarios, you'll develop a holistic perspective on applying Azure solutions. It will prepare you to analyze and address similar strategies during the exam.

Benefit 7: Refine Problem-solving and Analytical Skills

Engaging with complex scenarios and challenging questions in AZ-900 practice tests refines your problem-solving and analytical skills. You'll learn to dissect intricate problems, apply critical thinking, and leverage your understanding of Azure concepts to arrive at practical solutions.

Benefit 8: AZ-900 Practice Test Assesses Exam Readiness and Track Progress

Regularly taking AZ-900 practice tests allows you to assess your exam readiness and track your progress over time. By comparing your performance across multiple practice tests, you can gauge your improvement, identify areas of consistent strength. You can monitor your development in addressing previously challenging concepts.

Benefit 9: Familiarize Yourself with Azure Terminology and Technical Jargon

Exposure to a wide range of Azure terminology and technical jargon through practice tests enhances your familiarity with Azure-specific language. As you encounter various terms and concepts within the context of the exam, you develop a comprehensive understanding of Azure vocabulary. And also facilitating effective communication and collaboration within the Azure community and workplace.

Benefit 10: AZ-900 Practice Test Boosts Overall Exam Performance and Score

The cumulative impact of incorporating AZ-900 practice tests into your study routine is a substantial boost in your overall exam performance and score. By leveraging the benefits of practice tests, you build a robust foundation of Azure knowledge, refine your exam-taking skills. Moreover, practice tests elevates your confidence, positioning yourself for success in the AZ-900 exam and beyond.

Conclusion

Establishing a solid foothold in the world of Microsoft Azure demands a comprehensive understanding of fundamental cloud concepts. By adhering to the essential tips outlined in this guide and by attempting the AZ-900 practice test, you can equip yourself with the necessary knowledge, skills, and confidence to excel in the AZ-900 exam and embark on an enriching journey in the dynamic realm of Azure. Dedication, consistent practice, and a thorough understanding of Azure fundamentals are the keys to unlocking your full potential in cloud computing and Azure proficiency.

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Microsoft empowers health organizations with generative AI and actionable data insights

In the past year, AI has transformed what we thought was possible and opened up new avenues for groundbreaking transformations. From creating personalized treatment plans to extracting insights from XRAYs and MRIs, generative AI has made the concept of artificial intelligence real—and accessible, such as with Azure AI Health Bot. For the healthcare industry, this might mean the beginning of a transformative era that changes how healthcare is delivered and accessed—making precision medicine truly individualized, speeding up groundbreaking research for life threatening diseases, and finding new and innovative ways to improve patient care.

Making AI and machine learning real and actionable starts with the data being analytics ready. Healthcare data has been growing at an exponential rate and most healthcare organizations don’t know where to start with organizing that data. It is usually on-premises, siloed, and hard to navigate. The very first step would be to make this data accessible and normalize it in a way that makes it ready for analytics and AI in the cloud. Industry-specific solutions in Microsoft Fabric provide relevant solutions that unify data and insights for healthcare organizations through one common architecture and experience. Now available in preview, healthcare data solutions in Microsoft Fabric eliminate the costly, time-consuming process of stitching together a complex set of disconnected, multi-modal health data sources—text, images, video, and more—and provide a secure and governed way for organizations to access, analyze, and visualize data-driven insights across their organization.

We’re making several exciting announcements about new data and AI capabilities that will be introduced across the Microsoft Cloud for Healthcare to help health organizations improve patient experience, gain new insights with machine learning and AI, and handle health information securely. Features like de-identification service and getting insights from unstructured text will also be available soon in Fabric. We’re pleased to announce:

• General availability of multi-language support in Text Analytics for health, an Azure AI Language service. Healthcare organizations worldwide can use the Text Analytics for health service to extract meaningful insights in six languages in addition to English—Spanish, French, German, Italian, Portuguese, and Hebrew—making this technology more accessible to health organizations worldwide and improving health equity on a global scale.
• De-identification service (in preview) in Microsoft Fabric and Azure Health Data Services so organizations can de-identify medical data such that the resulting data retains its clinical relevance and distribution while also adhering to the HIPAA privacy rule. Our service supports unstructured text and will soon cover various other data types (structured, imaging, and MedTech). The service uses state-of-the-art machine learning models to automatically extract, redact, or surrogate over 30 entities—including HIPAA’s 18 protected health information (PHI) identifiers—from unstructured text such as clinical notes, messages, or clinical trial studies.
• Expansion of our Azure AI Health Bot in preview to allow healthcare organizations to build copilots for their healthcare professionals to further manage administrative and clinical workloads as well as improve patient experiences. Azure AI Health Bot is designed to help healthcare organizations create specialized chatbot experiences which are now powered by generative AI, enabling high-value conversational scenarios for the health and life sciences industry.
• Adding three new built-in models in preview to Azure AI Health Insights. These built-in models create actionable, chronological patient timelines based on clinical data and evidence, provide simplified, patient-friendly versions of clinical notes and reports, and surface radiology insights from radiology reports to help radiologists improve their workflow.

Building a healthcare ecosystem with a partner network 

In addition to our exciting product announcements, Wolters Kluwer also announced that its Health Language Platform, a Fast Healthcare Interoperability Resources (FHIR®) terminology server, will work with Microsoft Azure and Azure Health Data Services to help customers enrich and standardize their healthcare data with medical ontologies on Microsoft Azure.  

Customers onboarding to Azure will be able to access Wolters Kluwer’s Health Language Platform via Azure Marketplace to validate and translate their FHIR data so that it is ready for future analysis. Organizations can achieve semantic interoperability across multi-modal data sources to propel a range of use cases across healthcare. 

Working with our partner ecosystem, Microsoft is committed to continuing to develop healthcare technology that helps our customers use Microsoft Cloud to derive insights from their data and responsibly use AI. By connecting our customers with the right partners in our ecosystem and giving them access to Azure Marketplace, we want to ensure they have access to the right building blocks for their organizations use case.  

Real-world innovation in healthcare

By combining Microsoft Cloud for Healthcare services and tools, health organizations are coming up with new and innovative solutions to meet their unique needs.

For example, let’s say a researcher is working on a new drug for Alzheimer’s disease and needs to find suitable patients with specific symptoms and diagnoses to work on a hypothesis. First, they would de-identify their raw data so that they can use it for their research. If we look at this from the perspective of the clinician, they may want to look at a specific set of patients to see if there are any similarities and patterns that may help them with treatment plans for specific patients. Once they have established this, they can then gather from clinical notes they may have missed to ensure they have the full picture. When writing out their report and prescriptions for the patient, the clinician can opt to simplify their note using AI, making the note much easier for the patient to read as it will exchange complicated terminology for something easier to understand.

Next, a patient who has been diagnosed with Alzheimer’s disease takes the leading role. They are interested in finding more information about their prescription medications in the report which they were able to understand much more easily than before due to the lack of medical jargon. They find that their hospital website has a chatbot and are easily able to interact with them to get answers about their medications and set up appointments if they want.

And that’s just one possibility. Whether it’s finding new treatments, enhancing patient engagement, or optimizing workflows, Microsoft Cloud for Healthcare can help healthcare organizations achieve more.

Helping solve healthcare’s biggest problems

At Microsoft, we want to empower you in solving the challenges you face on a day-to-day basis—whether it be reducing clinician burnout or delighting your patients with personalized care—and to do these—allowing you to gain insights from your data and develop and deploy AI at scale.

With the help of Dataside, a Microsoft partner in Brazil, Oncoclínicas is using Microsoft’s Azure AI text analytics for health to extract data from non-structured fields like medical notes, anatomic pathology, genomic, and imaging reports like MRI. This data was then used by Dataside for various use cases such as clinical trial feasibility, a better understanding of the scenarios for pharmacoeconomics, and gaining a deeper understanding of group epidemiology and outcomes of interest.

“Text Analytics for health was a turning point for Grupo Oncoclínicas to scale our processes and to structure our clinical notes, exam reports and field analysis, which previously only depended on manual curation. Having a solution that works in Portuguese is key—most global solutions tend to only cater to English, thereby neglecting other languages. Accuracy in the native Portuguese allowed us to maintain a high level of accuracy while analyzing the unstructured text.”—Marcio Guimaraes Souza, Head of Data and AI at Grupo Oncoclínicas.

“We are excited to be collaborating with Microsoft to explore the potential of generative AI through the Azure AI Health Bot. This partnership aims to enhance healthcare content utilization at Ramsay Healthcare, offering a transformative way for healthcare professionals to engage with the vast clinical knowledge base. Our innovative solution facilitates seamless and efficient interactions, providing healthcare teams with quick access to answers, recommendations, and inventive troubleshooting solutions, all delivered through an intuitive chat interface. We are confident that it holds the promise to play a pivotal role in our daily operations, reducing time to find relevant content, and potentially revolutionizing the way we provide patient care.”—Towa Jexmark, Head of Innovation and Strategic Partnerships at Ramsay Sante.

Do more with your data with Microsoft Cloud for Healthcare

With Microsoft Cloud for Healthcare, organizations can transform their patient experience, discover new insights with the power of machine learning and AI, and manage PHI data with confidence. Enable your data for the future of healthcare innovation with Microsoft Cloud for Healthcare.

Source: microsoft.com

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The Power of Azure Arc: Revolutionizing Cloud Infrastructure

In the fast-paced world of modern technology, where businesses are continually seeking innovative solutions to streamline their operations and enhance efficiency, the importance of a robust and adaptable cloud infrastructure cannot be overstated. Enter Azure Arc, a game-changing platform developed by Microsoft that is poised to revolutionize the way organizations manage their cloud resources. In this article, we will delve deep into the world of Azure Arc, exploring its capabilities, benefits, and the impact it can have on your cloud infrastructure. Buckle up, as we embark on a journey to uncover the power of Azure Arc.

What is Azure Arc?

Before we dive into the specifics, let's establish a fundamental understanding of what Azure Arc is. In essence, Azure Arc is a hybrid and multicloud service offered by Microsoft Azure that extends Azure's management capabilities to other cloud platforms and on-premises environments. This means that with Azure Arc, you can seamlessly manage and govern your resources, irrespective of their location, using a unified control plane.

The Key Features of Azure Arc

Azure Arc comes armed with a plethora of features that make it a compelling choice for organizations seeking to optimize their cloud infrastructure. Here are some of the standout features:

1. Unified Management

Azure Arc offers a unified management experience, allowing you to manage resources across various cloud platforms and on-premises environments from a single, centralized interface. This simplifies the management of complex and distributed infrastructures, enhancing operational efficiency.

2. Resource Organization

With Azure Arc, you can organize your resources into logical groups, making it easier to manage and monitor them. This hierarchical structure provides a clear view of your resource hierarchy, facilitating efficient resource governance.

3. Policy Enforcement

One of the standout features of Azure Arc is its policy enforcement capabilities. You can define and enforce policies consistently across all your resources, ensuring compliance with regulatory standards and security best practices.

4. Data Protection and Backup

Azure Arc offers robust data protection and backup solutions, allowing you to safeguard your critical data and applications. Automated backup processes ensure that your data is secure and easily recoverable in case of unforeseen events.

5. Security and Compliance

Security is paramount in the digital age, and Azure Arc takes it seriously. It provides advanced security and compliance features, including threat detection, identity and access management, and vulnerability assessment, to safeguard your infrastructure against cyber threats.

Benefits of Azure Arc

Now that we've explored some of the features of Azure Arc, let's delve into the tangible benefits it can bring to your organization.

1. Flexibility and Scalability

Azure Arc's hybrid and multicloud capabilities offer unparalleled flexibility. You can seamlessly extend your on-premises infrastructure to the cloud and vice versa, allowing for easy scalability based on your organization's evolving needs.

2. Cost Optimization

By providing a unified management experience, Azure Arc helps optimize costs by eliminating the need for multiple management tools and reducing operational overhead. This, in turn, leads to significant cost savings in the long run.

3. Enhanced Security

With Azure Arc's comprehensive security and compliance features, your organization can stay ahead of security threats and ensure that your data and applications are protected at all times. This peace of mind is invaluable in today's threat landscape.

4. Streamlined Governance

Azure Arc simplifies resource governance by offering centralized policy enforcement and resource organization. This ensures that your organization can maintain compliance and governance standards consistently across all environments.

Real-World Applications

To truly understand the power of Azure Arc, let's take a look at some real-world applications that showcase its versatility and impact.

1. Retail Industry

In the retail sector, where data-driven decisions and seamless customer experiences are paramount, Azure Arc enables retailers to manage their inventory and customer data efficiently. It allows for the seamless integration of both physical and online stores, ensuring a consistent shopping experience for customers.

2. Healthcare Sector

The healthcare industry requires robust data management and security. Azure Arc assists healthcare providers in securely managing patient records, ensuring compliance with healthcare regulations, and enabling telemedicine services, all while maintaining the highest standards of data protection.

3. Financial Services

Financial institutions deal with vast amounts of sensitive data daily. Azure Arc empowers these organizations to securely manage their data, implement stringent security policies, and facilitate remote work, all while ensuring the utmost data integrity.

Conclusion

In conclusion, Azure Arc is more than just a tool for managing cloud resources; it is a transformative platform that empowers organizations to unlock the full potential of their cloud infrastructure. Its unified management, policy enforcement, and security features make it an invaluable asset in today's digital landscape. By embracing Azure Arc, your organization can achieve greater flexibility, cost optimization, and enhanced security, ultimately paving the way for innovation and growth.

As technology continues to evolve, staying ahead of the curve is crucial. Azure Arc is your ticket to a future-proof cloud infrastructure that can adapt to your organization's changing needs. Don't just keep up with the times—lead the way with Azure Arc.

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Microsoft Azure achieves HITRUST CSF v11 certification

The healthcare industry is undergoing a rapid transformation, driven by the increasing need for cloud computing to improve patient outcomes, capture cost efficiencies, and make it easier to coordinate care, especially for patients in remote areas. Cloud computing enables healthcare organizations to leverage advanced technologies such as artificial intelligence, machine learning, big data analytics, and Internet of Things to enhance their services and operations. However, cloud computing also brings new challenges and risks for securing and protecting sensitive healthcare data, such as electronic health records, medical images, genomic data, and personal health information. Healthcare organizations need to ensure that their cloud service providers meet the highest standards of security and compliance, as well as adhere to the complex and evolving regulations and frameworks that govern the healthcare industry.

Microsoft Azure committed to security and compliance in the healthcare industry

One of the most widely adopted and recognized frameworks for information protection in the healthcare industry is the HITRUST Common Security Framework (CSF). The HITRUST CSF is a comprehensive and scalable framework that integrates multiple authoritative sources, such as HIPAA, NIST, ISO, PCI, and COBIT, into a single set of harmonized controls. The HITRUST CSF provides a prescriptive and flexible approach for assessing and certifying the security and compliance posture of cloud service providers and their customers. Achieving HITRUST CSF certification demonstrates that a cloud service provider has implemented the best practices and controls to safeguard sensitive healthcare data in the cloud.

As healthcare organizations converge on the Dallas area for the HITRUST Collaborate 2023 event, providing secure and compliant cloud services for the healthcare industry is more important than ever. Microsoft Azure is committed to being a trusted partner for healthcare organizations in their digital transformation journey. Azure provides a comprehensive portfolio of cloud services that enable healthcare organizations to build innovative solutions that improve the entire healthcare experience. Azure also offers a range of capabilities that make it easier for healthcare organizations to achieve and maintain security and compliance in the cloud.

We are therefore proud to announce that Microsoft Azure has achieved HITRUST CSF v11.0.1 certification across 162 Azure services and 115 Azure Government services. All GA Azure regions across Azure and Azure Government clouds are included within this certification. This achievement reflects the continuous efforts by Azure to enhance its security and compliance offerings for customers in the healthcare industry.

HITRUST CSF v11.0.1 is the latest version of the framework that incorporates new requirements and updates from various authoritative sources, such as NIST SP 800-53 Rev 5, NIST Cybersecurity Framework v1.1, PCI DSS v3.2.1, FedRAMP High Baseline Rev 5, CSA CCM v3.0.1, GDPR, CCPA, and others. HITRUST CSF v11.0.1 also introduces new features and enhancements, such as maturity scoring model, risk factor analysis, inheritance program expansion, assessment scoping tool improvement, and more. Achieving HITRUST CSF v11.0.1 certification demonstrates the increasing commitment Azure has to providing secure and compliant cloud services for customers in the healthcare industry.

The HITRUST CSF v11.0.1 r2 Validated Assessment for Azure was performed by an independent third-party audit firm licensed under the HITRUST External Assessor program. The audit firm evaluated Azure for security policies, procedures, processes, and controls against the HITRUST CSF requirements applicable to cloud service providers. The audit firm also verified that security controls for Azure are implemented effectively and operate as intended. Azure customers can obtain the HITRUST CSF Letter of Certification, which contains the full scope of certified Azure offerings and regions, at the Service Trust Portal.

Microsoft Azure partners with HITRUST Alliance

In addition to today’s certification, Azure has also partnered in the past with HITRUST Alliance to release the HITRUST Shared Responsibility Matrix for Azure, which provides clarity around security and privacy responsibilities between Azure and its customers, making it easier for organizations to achieve their own HITRUST CSF certification. The matrix outlines which HITRUST CSF controls are fully managed by Azure, which are shared between Azure and customers, and which are solely the customers’ responsibility. The matrix also provides guidance on how customers can leverage the capabilities in Azure to meet their own security and compliance obligations.

Azure also supports the HITRUST Inheritance Program which empowers organizations to achieve more by significantly reducing the compliance cost and burden by enabling customers to externally inherit requirements from the Azure HITRUST CSF certification. The program allows customers to inherit up to 75 percent of applicable HITRUST CSF controls from the Azure certification scope without additional testing or validation by an external assessor. This reduces the time, effort, and resources required for customers to obtain their own HITRUST CSF certification or report on their compliance status using other frameworks or standards based on the HITRUST CSF. Azure has reviewed over 23,450 inheritance requests from customers since the program’s inception.

Azure has maintained the HITRUST CSF certification since November 2016. Azure was one of the first cloud service providers to achieve HITRUST CSF certification and has been continuously expanding its scope of certified services and regions. Azure is also one of the few cloud service providers that offer HITRUST CSF certified services in both public and government clouds. The Azure HITRUST CSF v11.0.1 certification is backward compatible with HITRUST CSF v9.1, v9.2, v9.3, v9.4, v9.5, and v9.6 certifications, offering support to a wide range of customers.

Azure HITRUST CSF certification

Azure is dedicated to helping healthcare organizations accelerate their digital transformation while ensuring security and compliance in the cloud. Azure provides a secure and compliant cloud platform that enables healthcare organizations to build innovative solutions that improve patient care, operational efficiency, and business agility. Azure also offers a variety of tools and resources that make it easier for healthcare organizations to achieve and maintain security and compliance in the cloud. The Azure HITRUST CSF certification is a testament to the commitment Azure has to be a trusted partner for healthcare organizations in their cloud journey.

Source: microsoft.com

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