#AzureOpenAI

[govindhtech]

Data Zones Improve Enterprise Trust In Azure OpenAI Service

The trust of businesses in the Azure OpenAI Service was increased by the implementation of Data Zones.

Data security and privacy are critical for businesses in today’s quickly changing digital environment. Microsoft Azure OpenAI Service provides strong enterprise controls that adhere to the strictest security and regulatory requirements, as more and more businesses use AI to spur innovation. Anchored on the core of Azure, Azure OpenAI may be integrated with the technologies in your company to assist make sure you have the proper controls in place. Because of this, clients using Azure OpenAI for their generative AI applications include KPMG, Heineken, Unity, PWC, and more.

With over 60,000 customers using Azure OpenAI to build and scale their businesses, it is thrilled to provide additional features that will further improve data privacy and security capabilities.

Introducing Azure Data Zones for OpenAI

Data residency with control over data processing and storage across its current 28 distinct locations was made possible by Azure OpenAI from Day 0. The United States and the European Union now have Azure OpenAI Data Zones available. Historically, variations in model-region availability have complicated management and slowed growth by requiring users to manage numerous resources and route traffic between them. Customers will have better access to models and higher throughput thanks to this feature, which streamlines the management of generative AI applications by providing the flexibility of regional data processing while preserving data residency within certain geographic bounds.

Azure is used by businesses for data residency and privacy

Azure OpenAI’s data processing and storage options are already strong, and this is strengthened with the addition of the Data Zones capability. Customers using Azure OpenAI can choose between regional, data zone, and global deployment options. Customers are able to increase throughput, access models, and streamline management as a result. Data is kept at rest in the Azure region that you have selected for your resource with all deployment choices.

Global deployments: With access to all new models (including the O1 series) at the lowest cost and highest throughputs, this option is available in more than 25 regions. The global backbone of the Azure resource guarantees optimal response times, and data is stored at rest within the customer-selected

Data Zones: Introducing Data Zones, which offer cross-region load balancing flexibility within the customer-selected geographic boundaries, to clients who require enhanced data processing assurances while gaining access to the newest models. All Azure OpenAI regions in the US are included in the US Data Zone. All Azure OpenAI regions that are situated inside EU member states are included in the European Union Data Zone. The upcoming month will see the availability of the new Azure Data Zones deployment type.

Regional deployments: These guarantee processing and storage take place inside the resource’s geographic boundaries, providing the highest degree of data control. When considering Global and Data Zone deployments, this option provides the least amount of model availability.

Extending generative AI apps securely using your data

Azure OpenAI allows you to extend your solution with your current data storage and search capabilities by integrating with hundreds of Azure and Microsoft services with ease. Azure AI Search and Microsoft Fabric are the two most popular extensions.

For both classic and generative AI applications, Azure AI search offers safe information retrieval at scale across customer-owned content. This keeps Azure’s scale, security, and management while enabling document search and data exploration to feed query results into prompts and ground generative AI applications on your data.

Access to an organization’s whole multi-cloud data estate is made possible by Microsoft Fabric’s unified data lake, OneLake, which is arranged in an easy-to-use manner. Maintaining corporate data governance and compliance controls while streamlining the integration of data to power your generative AI application is made easier by consolidating all company data into a single data lake.

Azure is used by businesses to ensure compliance, safety, and security

Content Security by Default

Prompts and completions are screened by a group of classification models to identify and block hazardous content, and Azure OpenAI is automatically linked with Azure AI Content Safety at no extra cost. The greatest selection of content safety options is offered by Azure, which also has the new prompt shield and groundedness detection features. Clients with more stringent needs can change these parameters, such as harm severity or enabling asynchronous modes to reduce delay.

Entra ID provides secure access using Managed Identity

In order to provide zero-trust access restrictions, stop identity theft, and manage resource access, Microsoft advises protecting your Azure OpenAI resources using the Microsoft Entra ID. Through the application of least-privilege concepts, businesses can guarantee strict security guidelines. Furthermore strengthening security throughout the system, Entra ID does away with the requirement for hard-coded credentials.

Furthermore, Managed Identity accurately controls resource rights through a smooth integration with Azure role-based access control (RBAC).

Customer-managed key encryption for improved data security

By default, the information that Azure OpenAI stores in your subscription is encrypted with a key that is managed by Microsoft. Customers can use their own Customer-Managed Keys to encrypt data saved on Microsoft-managed resources, such as Azure Cosmos DB, Azure AI Search, or your Azure Storage account, using Azure OpenAI, further strengthening the security of your application.

Private networking offers more security

Use Azure virtual networks and Azure Private Link to secure your AI apps by separating them from the public internet. With this configuration, secure connections to on-premises resources via ExpressRoute, VPN tunnels, and peer virtual networks are made possible while ensuring that traffic between services stays inside Microsoft’s backbone network.

The AI Studio’s private networking capability was also released last week, allowing users to utilize its Studio UI’s powerful “add your data” functionality without having to send data over a public network.

Dedication to Adherence

It is dedicated to helping its clients in all regulated areas, such as government, finance, and healthcare, meet their compliance needs. Azure OpenAI satisfies numerous industry certifications and standards, including as FedRAMP, SOC 2, and HIPAA, guaranteeing that businesses in a variety of sectors can rely on their AI solutions to stay compliant and safe.

Businesses rely on Azure’s dependability at the production level

GitHub Copilot, Microsoft 365 Copilot, Microsoft Security Copilot, and many other of the biggest generative AI applications in the world today rely on the Azure OpenAI service. Customers and its own product teams select Azure OpenAI because it provide an industry-best 99.9% reliability SLA on both Provisioned Managed and Paygo Standard services. It is improving that further by introducing a new latency SLA.

Announcing Provisioned-Managed Latency SLAs as New Features

Ensuring that customers may scale up with their product expansion without sacrificing latency is crucial to maintaining the caliber of the customer experience. It already provide the largest scale with the lowest latency with its Provisioned-Managed (PTUM) deployment option. With PTUM, it is happy to introduce explicit latency service level agreements (SLAs) that guarantee performance at scale. In the upcoming month, these SLAs will go into effect. Save this product newsfeed to receive future updates and improvements.

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Master Prompt Engineering on Azure OpenAI – FREE!

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Talk to Your SQL Database Using LangChain and Azure OpenAI

Excited to share a comprehensive review of LangChain, an open-source framework for querying SQL databases using natural language, in conjunction with Azure OpenAI's gpt-35-turbo model. This article demonstrates how to convert user input into SQL queries and obtain valuable data insights. It covers setup instructions and prompt engineering techniques for improving the accuracy of AI-generated results. Check out the blog post [here](https://ift.tt/s8PqQCc) to dive deeper into LangChain's capabilities and learn how to harness the power of natural language processing. #LangChain #AzureOpenAI #SQLDatabase #NaturalLanguageProcessing List of Useful Links: AI Scrum Bot - ask about AI scrum and agile Our Telegram @itinai Twitter -  @itinaicom

[unifycloudin]

Exciting news for businesses in India! UnifyCloud now offers Azure Open AI assessment services to help you unlock the power of artificial intelligence.

With UnifyCloud's expertise in cloud computing and data management, our Azure Open AI assessment services provide comprehensive insights into your organization's readiness for AI implementation.  Our team of experts will assess your existing infrastructure, applications, and data to identify opportunities for leveraging AI capabilities to drive innovation and efficiency.

Why choose UnifyCloud for Azure Open AI assessment services?  We have a proven track record of helping businesses embrace cutting-edge technologies. Our customized approach ensures that our assessment aligns with your specific business goals, and our recommendations are tailored to your organization's unique needs.

Don't miss out on the incredible benefits of AI!  Let UnifyCloud guide you on your journey to leveraging Azure Open AI for business success. Contact us today to schedule a consultation and unleash the power of AI in your organization! https://bit.ly/3An2u31

#UnifyCloud #AzureOpenAI #AIAssessment #TechnologyTransformation #IndiaBusinesses

[govindhtech]

Microsoft Azure OpenAI Data Zones And What’s New In Azure AI

Announcing the most recent Azure AI developments and the availability of Azure OpenAI Data Zones

Microsoft Azure AI is used by more than 60,000 clients, such as AT&T, H&R Block, Volvo, Grammarly, Harvey, Leya, and others, to propel AI transformation. The increasing use of AI in various sectors and by both small and large enterprises excites us. The latest features in Azure AI’s portfolio that offer more options and flexibility for developing and scaling AI applications are compiled in this blog. Important updates consist of:

There are Azure OpenAI Data Zones available for the US and EU that provide more extensive deployment choices.

Prompt Caching is available, Azure OpenAI Service Batch API is generally available, token generation has a 99% SLA, model prices are reduced by 50% through Provisioned Global, and lower deployment minimums are required for Provisioned Global GPT-4o models in order to scale effectively and minimize expenses.

Mistral’s Ministral 3B tiny model, Cohere Embed 3’s new healthcare industry models, and the Phi 3.5 family’s improved general availability offer more options and flexibility.

To speed up AI development, switch from GitHub Models to Azure AI model inference API and make AI app templates available.

Safely develop new enterprise-ready functionalities with AI.

United States and European Union Azure OpenAI Data Zones

Microsoft is presenting Azure OpenAI Data Zones, a new deployment option that gives businesses even more freedom and control over their residency and data privacy requirements. Data Zones, which are specifically designed for businesses in the US and the EU, enable clients to process and store their data inside predetermined geographic bounds, guaranteeing adherence to local data residency regulations while preserving peak performance. Data Zones, which cover several regions within these areas, provide a balance between the control of regional deployments and the cost-effectiveness of global deployments, facilitating enterprise management of AI applications without compromising speed or security.

By providing a solution that enables higher throughput and faster access to the newest AI models, including the most recent innovation from Azure OpenAI Service, this new feature streamlines the frequently difficult chore of maintaining data residency. Now, businesses can safely scale their AI solutions while adhering to strict data residency regulations by utilizing Azure’s strong infrastructure. Data Zones will soon be available for Provisioned and Standard (PayGo).

Updates for Azure OpenAI Services

Microsoft declared the Azure OpenAI Batch API for Global deployments to be generally available earlier this month. Using a separate quota, a 24-hour return time, and 50% less cost than Standard Global, developers can more effectively handle large-scale and high-volume processing activities using Azure OpenAI Batch API. One McKesson company, Ontada, is already using Batch API to process massive amounts of patient data from US oncology hospitals in an economical and efficient manner.

Additionally, it has enabled Prompt Caching on Azure OpenAI Service for the o1-preview, o1-mini, GPT-4o, and GPT-4o-mini models. By reusing recently viewed input tokens, developers can save expenses and latency with Prompt Caching. Applications that frequently employ the same context, like code editing or lengthy chatbot interactions, will find this capability especially helpful. Faster processing times and a 50% discount on cached input tokens for Standard offerings are provided by Prompt Caching.

It is reducing the basic deployment quantity for GPT-4o models to 15 Provisioned Throughput Units (PTUs) with further increments of 5 PTUs for the Provisioned Global deployment offering. To increase access to Azure OpenAI Service, it is also reducing the cost of Provisioned Global Hourly by 50%.

Microsoft is also launching a service level agreement (SLA) for token generation with a 99% latency. Especially at high volumes, this latency SLA guarantees that tokens are issued more quickly and reliably.

Customization and new models

With the addition of new models to the model library, it keeps broadening the selection of models. This month, it has several new models available, including models from Mistral and Cohere as well as models from the healthcare sector. Additionally, it is revealing that the Phi-3.5 family of models can be customized.

Advanced multimodal medical imaging models, such as MedImageInsight for image analysis, MedImageParse for image segmentation across imaging modalities, and CXRReportGen for detailed structured report generation, are part of the healthcare sector models. These models, which were created in partnership with Microsoft Research and industry partners, are intended to be adjusted and tailored by healthcare institutions to suit particular requirements, lowering the amount of data and processing power normally required to create such models from the ground up.

Mistral AI’s Ministral 3B: Ministral 3B, which emphasizes knowledge, commonsense thinking, function-calling, and efficiency, is a notable breakthrough in the sub-10B category. These models are designed for a wide range of applications, from creating specialized task workers to coordinating agentic processes, and they support up to 128k context length. Ministral 3B is a useful bridge for function-calling in multi-step agentic workflows when combined with bigger language models such as Mistral Large.

Cohere Embed 3: Embed 3, the multimodal AI search model that leads the market, is now accessible in the Azure AI Model Catalog! By enabling businesses to explore and analyze their massive volumes of data in any format, Embed 3’s capability to create embeddings from both text and images unlocks substantial value. This update transforms how companies navigate through complex materials like reports, product catalogs, and design files, positioning Embed 3 as the most potent and capable multimodal embedding model available.

Fine-tuning the Phi 3.5 family’s general availability, including Phi-3.5-mini and Phi-3.5-MoE: Phi family models can be easily customized to enhance basic model performance in a range of situations, including learning a new task or skill or improving response quality and consistency. When compared to models of the same size or next size up, Phi-3.5 models provide a more affordable and sustainable option because to their modest computing footprint and interoperability with cloud and edge. The Phi-3.5 family is already being adopted for non-connected scenarios and edge reasoning use cases. Today, developers may refine Phi-3.5-mini and Phi-3.5-MoE by utilizing serverless endpoints and the model as a platform offering.

Development of AI apps

Azure AI is being developed as an open, modular platform to enable developers to swiftly go from concept to code to cloud. Through the Azure AI model inference API, developers can now easily explore and access Azure AI models through the GitHub Marketplace. When developers are ready to customize and deploy, they can easily set up and log in to their Azure account to scale from free token usage to paid endpoints with enterprise-level security and monitoring without making any changes to the code. Developers can test out various models and compare model performance in the playground for free (usage limits apply).

In order to expedite the development of AI apps, it has unveiled AI App Templates. These templates are available to developers in Visual Studio, VS Code, and GitHub Codespaces. With a range of models, frameworks, languages, and solutions from vendors including Arize, LangChain, LlamaIndex, and Pinecone, the templates enable versatility. Developers can start with components or launch entire programs, allocating resources across Azure and partner services.

With these enhancements, developers can confidently scale AI systems, select the deployment choice that best suits their needs, and get started in their preferred environment immediately.

New tools to create enterprise-ready, safe AI applications

At Microsoft, its goal is to assist clients in using and developing trustworthy AI that is, AI that is private, secure, and safe. It is presenting two new features today that will help you confidently develop and grow AI solutions.

More than 1,700 models are available for developers to examine, assess, modify, and implement in the Azure AI model library. Although this wide range of options fosters creativity and adaptability, it can also pose serious difficulties for businesses that wish to make sure all implemented models meet their internal guidelines, security requirements, and legal requirements. Model selection and governance procedures can now be made simpler for Azure AI administrators by using Azure rules to pre-approve specific models for deployment from the Azure AI model catalog.

A comprehensive guide makes it easier to create custom policies for Azure OpenAI Service and other AI services, while pre-built policies for Models-as-a-Service (MaaS) and Models-as-a-Platform (MaaP) deployments are also included. When combined, these guidelines offer comprehensive protection for establishing an approved model list and implementing it throughout Azure AI Studio and Azure Machine Learning.

Developers may require access to on-premises resources or even resources not supported by private endpoints but nonetheless present in their custom Azure virtual network (VNET) in order to create models and applications. A load balancer called Application Gateway bases its routing choices on an HTTPS request’s URL. Using the HTTP or HTTPs protocol, Application Gateway will enable a private connection from the managed VNET to any resources.

It has been confirmed to enable private connections to Snowflake Database, Jfrog Artifactory, and Private APIs as of right now. Developers may access on-premises or bespoke VNET resources for their training, fine-tuning, and inferencing scenarios without sacrificing their security posture using Application Gateway in Azure Machine Learning and Azure AI Studio is currently available for public preview.

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Boost the development of AI apps with Cloud Modernization

Cloud Modernization

A new era of intelligent applications that can comprehend natural language, produce material that is human-like, and enhance human abilities has begun with the development of generative AI. But as businesses from all sectors start to see how AI may completely transform their operations, they frequently forget to update their on-premises application architecture, which is an essential first step.

Cloud migration

Cloud migration is significantly more advantageous than on-premises options if your company wants to use AI to improve customer experiences and spur growth. Numerous early adopters, including TomTom and H&R Block, have emphasized that their decision to start updating their app architecture on Azure was what prepared them for success in the AI era.

Further information to connect the dots was provided by a commissioned study by IDC titled “Exploring the Benefits of Cloud Migration and Cloud Modernization for the Development of Intelligent Applications,” which was based on interviews with 900 IT leaders globally regarding their experiences moving apps to the cloud. They’ll go over a few of the key points in this article.

Modernise or lag behind: The necessity of cloud migration driven by AI

Let’s say what is obvious: Artificial Intelligence is a potent technology that can write code, produce content, and even develop whole apps. The swift progress in generative AI technologies, such OpenAI’s GPT-4 has revolutionized the way businesses function and engage with their clientele.

However, generative AI models such as those that drive ChatGPT or image-generating software are voracious consumers of data. To achieve their disruptive potential, they need access to enormous datasets, flexible scaling, and immense computing resources. The computation and data needs of contemporary AI workloads are simply too much for on-premises legacy systems and compartmentalized data stores to handle.

Cloud Modernization systems, which are entirely managed by the provider, offer the reliable infrastructure and storage options required to handle AI workloads. Because of its nearly infinite scalability, apps can adapt to changing demand and continue to operate at a high level.

The main finding of the IDC survey was that businesses were mostly driven to move their applications to the cloud by a variety of benefits, such as enhanced security and privacy of data, easier integration of cloud-based services, and lower costs. Furthermore, companies can swiftly test, refine, and implement AI models because to the cloud’s intrinsic agility, which spurs innovation.

With its most recent version, the.NET framework is ready to use AI in cloud settings. Developers can use libraries like OpenAI, Qdrant, and Milvus as well as tools like the Semantic Kernel to include AI capabilities into their apps. Applications may be delivered to the cloud with excellent performance and scalability thanks to the integration with.

NET Aspire. H&R Block’s AI Tax Assistant, for instance, shows how companies may build scalable, AI-driven solutions to improve user experiences and operational efficiency. It was developed using.NET and Azure OpenAI. You may expedite development and boost the adoption of AI in all areas of your company operations by integrating. NET into your cloud migration plan.

Utilising cloud-optimized old on-premises programmes through migration and restructuring allows for the seamless scaling of computation, enormous data repositories, and AI services. This can help your business fully incorporate generative AI into all aspects of its data pipelines and intelligent systems, in addition to allowing it to develop generative AI apps.

Reach your AI goals faster in the cloud

The ambition of an organisation to use generative AI and the realisation of its full value through cloud migration are strongly correlated, according to a recent IDC study. Let’s dissect a few important factors:

Data accessibility: Consolidating and accessing data from several sources is made easier by cloud environments, giving AI models the knowledge they require for training and improvement.

Computational power: Elastic computing resources in the cloud may be flexibly distributed to fulfil complicated AI algorithm needs, resulting in optimal performance and cost effectiveness.

Collaboration: Data scientists, developers, and business stakeholders may work together more easily thanks to cloud-based tools, which speeds up the creation and application of AI.

Cloud migration speeds up innovation overall in addition to enabling generative AI. Cloud platforms offer an abundance of ready-to-use services, such as serverless computing, machine learning, and the Internet of Things, that enable businesses to quickly develop and integrate new intelligent features into their apps.

Adopt cloud-based AI to beat the competition

Gaining a competitive edge is the driving force behind the urgent need to migrate and modernise applications it’s not simply about keeping up with the times. Companies who use AI and the cloud are better positioned to:

Draw in elite talent Companies with state-of-the-art tech stacks attract the best data scientists and developers.

Adjust to shifting circumstances: Because of the cloud’s elasticity, organisations may quickly adapt to changing client wants or market conditions.

Accelerate the increase of revenue: Applications driven by AI have the potential to provide new revenue streams and improve consumer satisfaction.

Embrace AI-powered creativity by updating your cloud

Cloud migration needs to be more than just moving and lifting apps if it is to stay competitive. The key to unlocking new levels of agility, scalability, and innovation in applications is Cloud Modernization through rearchitecting and optimizing them for the cloud. Your company can: by updating to cloud-native architectures, your apps can:

Boost performance: Incorporate intelligent automation, chatbots, and personalised recommendations all enabled by AI into your current applications.

Boost output: To maximise the scalability, responsiveness, and speed of your applications, take advantage of cloud-native technology.

Cut expenses: By only paying for the resources you use, you can do away with the expensive on-premises infrastructure.

According to the IDC poll, most respondents decided to move their apps to the Cloud Modernization because it allowed them to develop innovative applications and quickly realize a variety of business benefits.

Boost the development of your intelligent apps with a cloud-powered AI

In the age of generative AI, moving and updating apps to the cloud is not a choice, but a requirement. Businesses that jump on this change quickly will be in a good position to take advantage of intelligent apps’ full potential, which will spur innovation, operational effectiveness, and consumer engagement.

The combination of generative AI and cloud computing is giving organizations previously unheard-of options to rethink their approaches and achieve steady growth in a cutthroat market.

Businesses may make well-informed decisions on their cloud migration and Cloud Modernization journeys by appreciating the benefits and measuring the urgency, which will help them stay at the forefront of technical advancement and commercial relevance.

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Utilize Azure AI Studio To Create Your Own Copilot

Microsoft Azure AI Studio

With Microsoft Azure AI Studio now broadly available, organisations may now construct their own AI copilots in the fast evolving field of AI technology. Organisations can design and create their own copilot using AI Studio to suit their specific requirements.

AI Studio speeds up the generative AI development process for all use cases, enabling businesses to leverage AI to create and influence the future.

An essential part of Microsoft’s copilot platform is Azure AI Studio. With Azure-grade security, privacy, and compliance, it is a pro-code platform that allows generative AI applications to be fully customised and configured. Utilising Azure AI services and tools, copilot creation is streamlined and accelerated with full control over infrastructure thanks to flexible and integrated visual and code-first tooling and pre-built quick-start templates.

With its simple setup, management, and API support, it eases the idea-to-production process and assists developers in addressing safety and quality concerns. The platform contains well-known Azure Machine Learning technology, such as prompt flow for guided experiences for speedy prototyping, and Azure AI services, such as Azure OpenAI Service and Azure AI Search. It is compatible with code-first SDKs and CLIs, and when demand increases, it can be scaled with the help of the AI Toolkit for Visual Studio Code and the Azure Developer (AZD) CLI.

AI Studios

Model Selection and API

Find the most appropriate AI models and services for your use case.

Developers can create intelligent multimodal, multilingual copilots with customisable models and APIs that include language, voice, content safety, and more, regardless of the use case.

More than 1600 models from vendors such as Meta, Mistral, Microsoft, and OpenAI are available with the model catalogue. These models include GPT 4 Turbo with Vision, Microsoft’s short language model (SLM) Phi3, and new models from Core42 and Nixtla. Soon to be released are models from NTT DATA, Bria AI, Gretel, Cohere Rerank, AI21, and Stability AI. The most popular models that have been packed and optimised for use on the Azure AI platform are those that Azure AI has curated. In addition, the Hugging Face collection offers a wide range of hundreds of models, enabling users to select the precise model that best suits their needs. And there are a tonne more options available!

With the model benchmark dashboard in Azure AI Studio, developers can assess how well different models perform on different industry-standard datasets and determine which ones work best. Using measures like accuracy, coherence, fluency, and GPT similarity, benchmarks evaluate models. Users are able to compare models side by side by seeing benchmark results in list and dashboard graph forms.

Models as a Platform (MaaP) and Models as a Service (MaaS) are the two model deployment options provided by the model catalogue. Whereas MaaP offers models deployed on dedicated virtual machines (VMs) and paid as VMs per-hour, MaaS offers pay-as-you-go per-token pricing.

Before integrating open models into the Azure AI collection, Azure AI Studio additionally checks them for security flaws and vulnerabilities. This ensures that model cards have validations, allowing developers to confidently deploy models.

Create a copilot to expedite the operations of call centers

With the help of AI Studio, Vodafone was able to update their customer care chatbot TOBi and create SuperAgent, a new copilot with a conversational AI search interface that would assist human agents in handling intricate customer queries.

In order to assist consumers, TOBi responds to frequently asked queries about account status and basic technical troubleshooting. Call centre transcripts are summarised by SuperAgent, which reduces long calls into succinct summaries that are kept in the customer relationship management system (CRM). This speeds up response times and raises customer satisfaction by enabling agents to rapidly identify new problems and determine the cause of a client’s previous call. All calls are automatically transcribed and summarised by Microsoft Azure OpenAI Service in Azure AI Studio, giving agents relevant and useful information.

When combined, Vodafone’s call centre is managing about 45 million customer calls monthly, fully resolving 70% of them. The results are outstanding. Customer call times have decreased by at least one minute on average, saving both customers’ and agents’ crucial time.

Create a copilot to enhance client interactions

With the help of AI Studio, H&R Block created AI Tax Assist, “a generative AI experience that streamlines online tax filing by enabling clients to ask questions during the workflow.”

In addition to assisting people with tax preparation and filing, AI Tax Assist may also provide tax theory clarification and guidance when necessary. To assist consumers in maximising their possible refunds and lowering their tax obligations, it might offer information on tax forms, deductions, and credits. Additionally, AI Tax Assist responds dynamically to consumer inquiries and provides answers to free-form tax-related queries.

Construct a copilot to increase worker output

Leading European architecture and engineering firm Sweco realised that employees needed a customised copilot solution to support them in their work flow. They used AI Studio to create SwecoGPT, their own copilot that offers advanced search, language translation, and automates document generation and analysis.

The “one-click deployment of the models in Azure AI Studio and that it makes Microsoft Azure AI offerings transparent and available to the user,” according to Shah Muhammad, Head of AI Innovation at Sweco, is greatly appreciated. Since SwecoGPT was implemented, almost 50% of the company’s staff members have reported greater productivity, which frees up more time for them to concentrate on their creative work and customer service.

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Azure Database for PostgreSQL AI-powered app development

PostgreSQL is a popular open-source database framework with many features and a reputation for reliability and adaptability. Microsoft Azure Database for PostgreSQL is a highly scalable and convenient cloud-based solution that uses PostgreSQL. You can concentrate on creating incredible, AI-powered applications instead of worrying about maintaining your PostgreSQL instances thanks to Azure’s fully managed service.

In order to provide you with more information about how Azure Database for PostgreSQL enables customers to migrate their PostgreSQL databases and create intelligent applications, this blog will expose you to a variety of new learning opportunities, including two Cloud Skills Challenges. As if that weren’t thrilling enough, finishing a challenge entitles you to a drawing for a fantastic prize. Let’s move forward now!

Azure Database for PostgreSQL backup

Seamless development of apps and database migration

Embrace smooth deployments, automated patching, and built-in high availability in place of laborious maintenance chores. One completely managed service that makes moving current PostgreSQL databases to the cloud easier is Azure Database for PostgreSQL. You can concentrate on your apps while we take care of the scaling, backups, and patching.

Adaptability to different needs and budgets is ensured by seamless integration with PostgreSQL, which reduces code modifications during the transition. Data and schema transfers to the cloud are made simple with Azure Database for PostgreSQL’s migration capabilities.

Beyond migration, Azure Database for PostgreSQL facilitates the creation of applications driven by artificial intelligence. Effective storage and querying of vector embeddings is made possible by its native support for the pgvector extension, which is crucial for AI and machine learning applications. The service offers developers a comprehensive toolkit for creating intelligent applications by integrating seamlessly with existing Azure AI services, including Microsoft Azure AI Language, Microsoft Azure AI Translator, Azure Machine Learning, and Azure OpenAI Service.

Furthermore, the scalability of the service guarantees optimal performance as AI workloads increase, preserving cost effectiveness throughout the development process. All in all, Azure Database for PostgreSQL offers a complete solution for cloud migration as well as for developing potent artificial intelligence applications.

Here are a few salient characteristics:

High availability: Automatic patching, updates, zone-redundant high availability, and up to 99.99% uptime are all guaranteed.

Automating performance: Find ways to enhance query performance with suggestions for query stores and indexes by getting an analysis of your database workloads.

Security: consists of Azure IP Advantage, which shields companies and developers using Azure from intellectual property risks, and Microsoft Defender, which protects your data using open-source relational databases.

Azure AI add-on: Create and save vector embeddings, use Azure AI services, and develop AI-enabled applications straight from the database.

Support for migration: To facilitate the switch from Oracle to Azure databases for PostgreSQL, tools are offered.

Budget-friendly: offers complete database optimization and monitoring solutions that can result in operational savings of up to 62% when compared to on-premises, thus lowering the total cost of ownership.

Create AI Applications using Azure PostgreSQL Databases

This learning path, which is intended for developers who are interested in integrating AI into their PostgreSQL applications on Azure, explains how to do so by utilizing the Azure AI extension for Azure Database for PostgreSQL.

You will obtain a thorough understanding of the Azure AI extension and its numerous functions by finishing this learning route. Learn the differences between extractive, abstractive, and query-focused summarization, assess several summarization methods made available by Azure AI services and the azure_ai extension, and apply generative AI summarization methods to data in a PostgreSQL database. During this practical training, you will get the ability to use Azure AI services and the azure_ai extension to create intelligent apps that can condense and enlighten complex content into manageable summaries.

Set up PostgreSQL to be migrated to Azure Database

The knowledge and abilities you’ll need to operate with Azure Database for PostgreSQL efficiently are provided via this study route. Before getting into more practical topics like connecting to databases, running queries, and making sure security is tight, it starts with a basic overview of PostgreSQL architecture and fundamental ideas.

Additionally, you will learn how to use stored procedures and functions to repurpose code, as well as how to design and manage databases, schemas, and tables. You’ll feel more confident setting up, maintaining, and moving current PostgreSQL databases to Azure once you understand how Azure Database for PostgreSQL handles ACID transactions and write-ahead logging for data integrity and durability.

To win Azure prizes, finish timed challenges

Two matching Azure Database for PostgreSQL Cloud Skills Challenges have also been put together to go along with these learning courses. Cloud Skills Challenges combine an interactive learning sprint with a friendly competition between you and thousands of your friends worldwide, in contrast to the self-paced, isolated activities that characterize learning paths. In order to provide students with a comprehensive education, these immersive, gamified learning environments combine lessons, exams, and practical tasks.

If you finish at least one of these tasks before the timer goes off, you’ll instantly be added to a drawing to win one of 20 fantastic Azure prizes. Join the competition when these challenges begin on June 11, 2024!

Attend POSETTE 2024 to network with PostgreSQL specialists

POSETTE 2024, originally known as Citus Con and hosted by Microsoft, is an exciting development conference devoted to anything PostgreSQL. This is a special chance to hear from professionals, connect with other Postgres fans, and discover the newest developments in database technology.

Microsoft will be demonstrating their dedication to the open-source database system as a major participant in the PostgreSQL community. During a session titled “Azure Database for PostgreSQL: A Look Ahead,” attendees may anticipate hearing from Azure’s experts about their plans for the service and how it will work with other Azure products.

POSETTE, or Postgres Open Source Ecosystem Talks, Training, and Education, is a free online event that takes place from June 11 to June 13, 2024. It features four distinct livestreams. All scheduled talks will be available for viewing online right away following the event; registration is not required. Seize the opportunity to engage with the Microsoft team and discover how we are propelling PostgreSQL’s advancement in the cloud.

Take your Azure Database for PostgreSQL adventure to the next level

PostgreSQL and Azure Database for PostgreSQL are an ideal combination for creating cutting-edge, scalable, and AI-powered applications, regardless of your level of development experience. Azure Database for PostgreSQL enables customers to create complex AI applications and migrate to the cloud with ease. It does this by providing strong migration tools and a smooth connection with AI and machine learning services.

Check out the POSETTE 2024 livestreams to discover more about what you can achieve with the most sophisticated open-source database in the world, and get started with Azure’s two learning tracks and their corresponding Cloud Skills Challenges to enter a drawing for awesome Azure prizes.

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AI Collaboration Between Microsoft Office and SAP Production

Microsoft Office and SAP and have worked together for over 30 years to develop breakthrough business solutions and accelerate business transformation for thousands of clients. Microsoft Cloud leads the market for SAP workloads on the cloud, including RISE with SAP. At SAP Sapphire 2024, Microsoft are pleased to present more incredible innovation to their collaborative customers.

SAP and Microsoft

This blog discusses Microsoft Office and SAP latest announcements and how they assist customers accelerate business transformation.

Microsoft 365 Copilot and SAP Joule announce AI integration.

Microsoft Office and SAP are expanding our partnership at SAP Sapphire by combining Joule and Microsoft Copilot into a unified experience that lets employees get more done in the flow of their work by seamlessly accessing SAP and Microsoft 365 business applications.

Customers want AI assistants to complete queries regardless of data location or system. The generative AI solutions Joule and Copilot for Microsoft 365 will integrate intuitively to let users locate information faster and complete activities without leaving the site.

Copilot for Microsoft 365 users can use SAP Joule to access SAP S/4HANA Cloud, Success Factors, and Concur data. Someone utilizing SAP Joule in a SAP application can use Copilot for Microsoft 365 without context switching.

Microsoft AI and SAP RISE enable business change

SAP systems store mission-critical data that powers fundamental business processes and can benefit from AI insights, automation, and efficiencies. Microsoft Cloud, the most reliable, complete, and integrated cloud, may help you attain these benefits. SAP may improve RISE with a variety of Microsoft AI services to maximize business results tailored to your needs:Credit to Microsoft Azure

Collaboration with SAP and Microsoft Azure on AI innovation: SAP’s BTP platform enhances RISE with SAP. We announced SAP AI Core, an essential aspect of BTP, including Generative AI Hub and Joule on Microsoft Azure in West Europe, US East, and Sydney, alongside SAP.

Azure makes it easy for BTP users to add insight to their financial, supply chain, and order-to-cash processes. SAP users can use the most popular large language models like GPT-4o in SAP Generative AI Hub, available only on Azure through Azure OpenAI Service.

Microsoft Copilot unlocks end-user productivity: SAP clients may utilize Copilot for Microsoft 365 in Microsoft Word, PowerPoint, Excel, and PowerBI to unlock insights and complete tasks faster. The SAP plugin in Microsoft Copilot Studio lets you customize Copilot with your data, processes, and policies. You can customize Copilot to link to SAP systems and retrieve expenditure, inventory, etc. data. New Copilot connectors and agent features unveiled at Microsoft Build last week enable this.

Microsoft azure and SAP

Enabling AI transformation with Azure AI services: SAP customers utilizing Azure may quickly construct generative AI applications using the Azure OpenAI Service and SAP data. Azure AI offers over 1,600 frontier and open models, including the latest from OpenAI, Meta, and others, giving you the flexibility to choose the best model for your use case. Today, over 50,000 customers use Azure AI, demonstrating its rapid growth.

Microsoft Office and SAP

OpenAI combined with business process automation solutions like Microsoft Power Platform, which offers SAP connectors, can boost productivity by making SAP data interaction easier and task completion faster.

Cloud Application programming model (CAP) and SAP BTP, AI Core allow RISE with SAP clients to use OpenAI services.

Automated SAP attack disruption with AI: Microsoft Security Copilot helps security and IT professionals discover and manage SAP-related cyberthreats quickly.

Microsoft Sentinel for SAP, certified for RISE with SAP, automatically detects financial fraud and disables the native SAP and Microsoft Extra account to prevent the cyberattacked from transferring funds without additional intervention.

Greater SAP BTP on Azure availability increases choice and flexibility.

SAP and Microsoft recently announced the expansion of SAP BTP to six new Azure regions (Brazil, Canada, India, the UK, Germany, and one to be announced) and additional BTP services in seven public Microsoft Azure regions. After completion, SAP Business Technology Platform will run in 13 Microsoft Azure regions and offer all the most requested BTP services.

The expanded service availability, based on customer demand, allows customers to integrate SAP Cloud ERP and SAP BTP services with Microsoft Azure services, including AI services from both companies, in the same data Centre region, making AI innovation and business transformation easier.

New and powerful Azure SAP HANA infrastructure choices

The Azure M-Series Mv3 family of memory-optimized virtual machines, announced last year, provides faster insights, more uptime, a lower total cost of ownership, and better price-performance for SAP HANA workloads with Azure IaaS and SAP RISE. These VMs, which enable up to 32 TB of memory, use 4th-generation Intel Xeon Scalable CPUs and Azure Boost, Azure’s latest infrastructure innovation.

They are glad to build on this investment and announce that the Mv3 Very High Memory (up to 32TB) offering is generally available and the Mv3 High Memory offering (up to 16TB) is under preview.

SAP Signavio and Microsoft collaborate to accelerate transformation

Microsoft Office and SAP collaborate to help customers adopt S/4HANA and RISE on Azure. SAP Signavio Process Insights, part of SAP’s cloud-based process transformation suite, helps firms quickly identify SAP business process improvements and automation opportunities. SAP Signavio Process Insights on Microsoft Azure accelerates S/4HANA for ECC customers, unlocking innovation with Microsoft.

New Microsoft Teams connectors simplify business collaboration. For years, Microsoft Office and SAP have collaborated to boost productivity by combining SAP mission-critical data with Microsoft Teams and Microsoft 365.

Upcoming Microsoft Teams S/4HANA app features

S/4HANA Copilot plugin: Using natural language, users may retrieve S/4HANA sales information, order status, sales quotes, and more using Copilot for Microsoft 365.

Card Loop Modifiable Components: Exchange intelligent cards with Outlook and Teams (restricted pre-release).Credit to Microsoft Azure

Message extension for Teams based on search: Users can quickly look up and add S/4HANA data without ever leaving Teams.

Search SAP Community and share content : Search SAP Community and share content with coworkers in Microsoft Teams without leaving the app. Click the three dots below the text box, open the SAP S/4HANA app, and input your search phrase in the popup window while speaking with a colleague or group. Select a topic from the results list and email your peers.

Share Card to MS Teams: Microsoft Teams provides a collaborative view that opens application content in a new window for meaningful conversations with coworkers. S/4HANA is available in Outlook and Microsoft 365 Home.

New release: SuccessFactors for Microsoft Teams Chatbot-based HR task reminders A private message from the SuccessFactors Teams chatbot can assist you execute HR activities in Teams or direct you to SuccessFactors online for more sophisticated tasks.

Command-Based Quick Actions: Request and give feedback to coworkers, manage time entries, view learning assignments and approvals, access employee and management self-services, and more!

Microsoft and SAP partnership clients modernize identification

They stated earlier this year that they are working with SAP to develop a solution to convert identity management scenarios from SAP Identity Management (IDM) to Microsoft Entrap ID. Very thrilled to share that migration guidance will be available soon.

Microsoft Office and SAP partnership for customer success

Product innovation that drives customer success and business outcomes is thrilling. Microsoft was an early adopter of RISE with SAP internally and is happy to have helped thousands of clients speed their business transformation using the Microsoft Cloud.

To spur continuous innovation, Hilti Group, a construction provider, transformed its massive SAP landscape to RISE with SAP on Microsoft Azure. It also changed its SAP S/4HANA ERP application from 12 to 24 terabytes, closing its on-premises datacenter to utilize Azure only. Using RISE with SAP, which combines project management, technical migration, and premium engagement services, Hilti aimed to be among the first.

The company’s massive Microsoft Office and SAPlandscape, which underpins its transactional operations, proved to be the perfect platform for evolving its business transformation solution an expedited, on-demand solution.

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Azure AI Whisper model, which is now widely accessible

Azure AI Whisper model

One of the hardest things for computers to process is still human speech. With thousands of languages spoken throughout the world, businesses frequently find it difficult to select the appropriate technologies for audio conversation analysis and understanding while maintaining the necessary data security and privacy safeguards. Businesses now find it simpler to examine each consumer interaction and extract useful insights from it because of generative AI.

In order to assist clients in making sense of their voice data, Azure AI provides an industry-leading portfolio of AI services. Specifically, their speech-to-text service via Azure OpenAI Service and Azure AI Speech provides a range of unique features. Customers have benefited greatly from these features, which have made it possible to develop multilingual speech transcription and translation for lengthy audio files as well as near-real-time and real-time support for customer care agents.

They are happy to announce today’s general availability of Azure AI Whisper model. Developers can use OpenAI’s Whisper speech to text model to transcribe audio files. With Azure’s enterprise-readiness guarantee in place, developers can now start utilizing the publicly available Whisper API in Azure OpenAI Service and Azure AI Speech services for production workloads. The general release of all our speech-to-text models gives customers more choice and flexibility for AI-powered transcription and other speech scenarios.

Thousands of users in a variety of industries, including healthcare, education, finance, manufacturing, media, agriculture, and more, have been using the Whisper API in Azure since it was made available to the general public. They are using it to translate and transcribe audio into text in many of the 57 supported languages. Whisper is used to handle call center conversations, mine audio and video data for useful insights, and add captions to video and audio content for accessibility.

In order to expand our offering and meet the needs of our clientele who are looking to develop workflows and use cases utilizing speech technologies and LLMs. They are constantly adding OpenAI models to Azure. Consider an end-to-end contact center workflow with automated call routing, real-time agent assistance copilots, automated post-call analytics, and a text or voice self-service copilot that has human-like conversations with end users. The generative AI-powered end-to-end workflow could revolutionize call center productivity

Azure Open AI Whisper model

With the Azure OpenAI Service, developers can run Azure AI Whisper model, which replicates its features, such as quick processing, multilingual support, and transcription and translation abilities. For workloads and use-cases that require speed, OpenAI Whisper in the Azure OpenAI Service is the best option for processing smaller files.

Azure AI Speech uses the OpenAI Whisper model

The Azure AI Speech batch transcription API and OpenAI’s Whisper model can be used by users of Azure AI Speech. Customers can now quickly and easily transcribe large amounts of audio content for batch workloads that don’t require a lot of time.

Additional features that developers utilizing Whisper in Azure AI Speech can take advantage of include the following:

Processing large files up to 1 GB in size and handling 1000 files in a single request, as well as processing multiple audio files at once, are both possible.

Speaker diarization enables programmers to discern between various speakers, faithfully record their speech, and produce a transcription of audio files that is better structured and organized.

Finally, developers can refine the Whisper model using audio and human-labeled transcripts by using Custom Speech in Speech Studio or through an API.

Whisper in Azure AI Speech is being used by customers for a variety of purposes, including post-call analysis and the extraction of insights from audio and video recordings.

Using Whisper for the first

OpenAI Studio on Azure

Through the Azure OpenAI Studio, developers who would rather utilize the Whisper model in the Azure OpenAI Service can do so.

Users must apply for access in order to use Azure OpenAI Service.

After being accepted, create an Azure OpenAI Service resource via the Azure portal.

Users can start using Whisper as soon as the resource is created.

Azure AI Speech Studio

The batch speech-to-text feature in Azure AI Speech Studio allows developers who would rather use the Whisper model in Azure AI Speech to access it.

With the batch speech to text try-out, you can quickly assess which model might be more appropriate for your particular situation by contrasting the Whisper model’s output with that of an Azure AI Speech model.

The Whisper model is a fantastic addition to Azure AI’s extensive range of capabilities. Thye anticipate seeing the creative ways in which developers will utilize this new offering to delight users and increase business productivity.

OpenAI Whisper Whisper model

The OpenAI Whisper speech-to-text model can transcribe audio files. An extensive dataset of English text and audio is used to train the model. The model is best suited for transcribing English-speaking audio files. Transcribing audio files with spoken language in different languages is another application for this model. English text is the model’s output.

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