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Explore how Azure OpenAI Service is changing the game for businesses! From natural language processing to intelligent automation, discover how you can build smarter apps that deliver real-time results and customer satisfaction. Unlock innovation and efficiency with AI-powered solutions tailored for your business success.
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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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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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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