#AzureOpenAIService

[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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[aiandemily]

大公開! ソフトバンクで生成AIどう活用する?! 社内GPT活用事例４選

[kevalsing]

Gpt 4 in Azure OpenAI Service

Chatbots have become increasingly popular in recent years as businesses seek to improve customer experiences by providing immediate support and assistance. However, building a chatbot that can understand and respond to human language in a natural and intuitive way can be a daunting task. That’s where ChatGPT comes in. ChatGPT is a large language model trained by OpenAI that can generate…

[pavel-nosok]

Microsoft Is Expanding Access to Its Azure OpenAI Service, With ChatGPT Available 'Soon'

Generative artificial intelligence’s path toward real-world usefulness just became a bit more clear. On Monday, Microsoft announced it’s widening access to its Azure OpenAI Service, a move likely to increase business uses of Open AI’s popular GPT-3.5, Codex, and DALL E 2 systems. ChatGPT, the impressive chatbot that… Read more…

[govindhtech]

NASA’s Earth Copilot Uses Microsoft AI To Share Tricky Data

From inquiries to revelations: Microsoft AI capabilities are included into NASA’s new Earth Copilot to democratize access to intricate data.

NASA satellites orbit the planet daily, gathering data to help us comprehend it. This large Earth Science data set on climate change and wildfires can benefit science, politics, agriculture, urban planning, and disaster relief.

It can be difficult to navigate the more than 100 petabytes of data gathered, which is why NASA and Microsoft have partnered to investigate the usage of a custom copilot utilizing Azure OpenAI Service to create NASA’s Earth Copilot. This might revolutionize how users engage with Earth’s data.

Because geospatial data is so complicated, navigating it frequently calls for some technical know-how. Because of this, only a few number of scientists and academics often have access to this data. These complications only increase as NASA gathers more data from additional satellites, which could further restrict the pool of possible researchers and developers of apps that could advance civilization.

NASA decided to make its data more usable and accessible after understanding this issue. NASA’s Office of the Chief Science Data Officer intends to democratize data access for scientists, educators, politicians, and the public by reducing technical barriers.

The difficulty: Handling the intricacy of the data

NASA’s Earth Science Data Systems Program is in charge of gathering an astounding array of data from instruments and sensors in orbit. This information covers a wide range of topics, including ocean temperatures, land cover changes, and atmospheric conditions. Nevertheless, the magnitude and intricacy of this data can be debilitating. Very few non-technical users have the specific skills necessary to navigate technological interfaces, comprehend data formats, and grasp the nuances of geospatial analysis, which are necessary for many people to uncover and extract insights. AI might expedite this procedure, cutting the amount of time needed to extract insights from Earth’s data to just a few seconds.

This problem has practical ramifications; it is not merely a convenience issue. Policymakers who wish to investigate deforestation trends in order to enact environmental restrictions, or scientists who must evaluate past hurricane data in order to enhance prediction models, might not have easy access to the information they require. Many industries are impacted by this inaccessibility, including as agriculture, urban planning, and disaster relief, where prompt insights from spaceborne data could have a big impact.

Furthermore, NASA is always confronted with the task of developing new tools to manage and make sense of this expanding library as new satellites with new instrumentation continue to launch and gather more data. The organization looked into cutting-edge technology that could improve accessibility and speed up data discovery, allowing more individuals to interact with the data and gain fresh perspectives.

The answer is Microsoft Azure’s AI-powered data access

In order to tackle these issues, NASA IMPACT collaborated with Microsoft to create Earth Copilot, an AI-powered customer copilot that may make data access easier and inspire more people to engage with its Earth Science data. Together, they created the proof of concept AI model that would revolutionize how people search for, find, and analyze NASA’s geospatial data by utilizing Microsoft’s Azure cloud platform and cutting-edge AI capabilities.

Cloud-based solutions such as Azure OpenAI Service, which give developers access to strong AI models and natural language processing capabilities so they can include intelligent, conversational AI into their apps, are essential to NASA’s Earth Copilot. This strategy enables NASA to incorporate AI into VEDA, its current data analysis platform. When combined, these technologies facilitate users’ ability to find, search for, and evaluate Earth Science data.

Earth Copilot combines these technologies to allow people to utilize plain language queries to connect with NASA’s data repository. Alternatively, they might only pose queries like “How did the COVID-19 pandemic impact air quality in the United States?” or “What was the impact of Hurricane Ian in Sanibel Island?” After that, AI will obtain pertinent datasets, resulting in a smooth and user-friendly process.

Open research through democratizing data

A wider spectrum of users may now interact with NASA’s science data with the solution developed due to the partnership between Microsoft and NASA IMPACT. The scientific community will profit greatly from this since researchers may now focus more on analysis and discoveries and less on retrieving data. For instance, agricultural professionals can learn more about soil moisture levels to enhance crop management, and climate scientists can rapidly access historical data to examine trends.

Involving pupils in Earth Science in real-world circumstances can spark their curiosity and create future scientists and engineers. Policymakers can make informed decisions on disaster preparedness, urban growth, and climate change with the latest data.

This AI prototype supports NASA’s Open Science program, which promotes scientific research transparency, diversity, and cooperation. NASA and Microsoft are laying the groundwork for a new era of discovery by removing obstacles to data discovery. This era will allow anybody who is interested in the world to explore and gain new insights.

Looking Ahead: Connecting the dots between ideas and data

Currently, NASA scientists and researchers can use the NASA Earth Copilot to investigate and evaluate its capabilities. Strict evaluations are necessary for every ethical AI technology implementation to guarantee that the data and results cannot be abused. Following a phase of internal testing and assessments, the NASA IMPACT team will investigate how to incorporate this feature into the VEDA platform.

This partnership exemplifies how technology can empower individuals, spur creativity, and bring about constructive change. Such solutions will be crucial to guaranteeing that the advantages of data are widely disseminated, allowing more people to interact with, evaluate, and act upon the information that influences its world.

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[govindhtech]

Updates to Azure AI, Phi 3 Fine tuning, And gen AI models

Introducing new generative AI models, Phi 3 fine tuning, and other Azure AI enhancements to enable businesses to scale and personalise AI applications.

All sectors are being transformed by artificial intelligence, which also creates fresh growth and innovation opportunities. But developing and deploying artificial intelligence applications at scale requires a reliable and flexible platform capable of handling the complex and varied needs of modern companies and allowing them to construct solutions grounded on their organisational data. They are happy to share the following enhancements to enable developers to use the Azure AI toolchain to swiftly and more freely construct customised AI solutions:

Developers can rapidly and simply customise the Phi-3-mini and Phi-3-medium models for cloud and edge scenarios with serverless fine-tuning, eliminating the need to schedule computing.

Updates to Phi-3-mini allow developers to create with a more performant model without incurring additional costs. These updates include a considerable improvement in core quality, instruction-following, and organised output.

This month, OpenAI (GPT-4o small), Meta (Llama 3.1 405B), and Mistral (Large 2) shipped their newest models to Azure AI on the same day, giving clients more options and flexibility.

Value unlocking via customised and innovative models

Microsoft unveiled the Microsoft Phi-3 line of compact, open models in April. Compared to models of the same size and the next level up, Phi-3 models are their most powerful and economical small language models (SLMs). Phi 3 Fine tuning a tiny model is a wonderful alternative without losing efficiency, as developers attempt to customise AI systems to match unique business objectives and increase the quality of responses. Developers may now use their data to fine-tune Phi-3-mini and Phi-3-medium, enabling them to create AI experiences that are more affordable, safe, and relevant to their users.

Phi-3 models are well suited for fine-tuning to improve base model performance across a variety of scenarios, such as learning a new skill or task (e.g., tutoring) or improving consistency and quality of the response (e.g., tone or style of responses in chat/Q&A). This is because of their small compute footprint and compatibility with clouds and edges. Phi-3 is already being modified for new use cases.

Microsoft and Khan Academy are collaborating to enhance resources for educators and learners worldwide. As part of the partnership, Khan Academy is experimenting with Phi-3 to enhance math tutoring and leverages Azure OpenAI Service to power Khanmigo for Teachers, a pilot AI-powered teaching assistant for educators in 44 countries. A study from Khan Academy, which includes benchmarks from an improved version of Phi-3, shows how various AI models perform when assessing mathematical accuracy in tutoring scenarios.

According to preliminary data, Phi-3 fared better than the majority of other top generative AI models at identifying and fixing mathematical errors made by students.

Additionally, they have optimised Phi-3 for the gadget. To provide developers with a strong, reliable foundation for creating apps with safe, secure AI experiences, they launched Phi Silica in June. Built specifically for the NPUs in Copilot+ PCs, Phi Silica expands upon the Phi family of models. The state-of-the-art short language model (SLM) for the Neural Processing Unit (NPU) and shipping inbox is exclusive to Microsoft Windows.

Today, you may test Phi 3 fine tuning in Azure AI

Azure AI’s Models-as-a-Service (serverless endpoint) feature is now widely accessible. Additionally, developers can now rapidly and simply begin developing AI applications without having to worry about managing underlying infrastructure thanks to the availability of Phi-3-small via a serverless endpoint.

The multi-modal Phi-3 model, Phi-3-vision, was unveiled at Microsoft Build and may be accessed via the Azure AI model catalogue. It will also soon be accessible through a serverless endpoint. While Phi-3-vision (4.2B parameter) has also been optimised for chart and diagram interpretation and may be used to produce insights and answer queries, Phi-3-small (7B parameter) is offered in two context lengths, 128K and 8K.

The community’s response to Phi-3 is excellent. Last month, they launched an update for Phi-3-mini that significantly enhances the core quality and training after. After the model was retrained, support for structured output and instruction following significantly improved.They also added support for |system|> prompts, enhanced reasoning capability, and enhanced the quality of multi-turn conversations.

They also keep enhancing the safety of Phi-3. In order to increase the safety of the Phi-3 models, Microsoft used an iterative “break-fix” strategy that included vulnerability identification, red teaming, and several iterations of testing and improvement. This approach was recently highlighted in a research study. By using this strategy, harmful content was reduced by 75% and the models performed better on responsible AI benchmarks.

Increasing model selection; around 1600 models are already accessible in Azure AI They’re dedicated to providing the widest range of open and frontier models together with cutting-edge tooling through Azure AI in order to assist clients in meeting their specific cost, latency, and design requirements. Since the debut of the Azure AI model catalogue last year, over 1,600 models from providers such as AI21, Cohere, Databricks, Hugging Face, Meta, Mistral, Microsoft Research, OpenAI, Snowflake, Stability AI, and others have been added, giving us the widest collection to date. This month, they added Mistral Large 2, Meta Llama 3.1 405B, and OpenAI’s GPT-4o small via Azure OpenAI Service.

Keeping up the good work, they are happy to announce that Cohere Rerank is now accessible on Azure. Using Azure to access Cohere’s enterprise-ready language models Businesses can easily, consistently, and securely integrate state-of-the-art semantic search technology into their applications because to AI’s strong infrastructure. With the help of this integration, users may provide better search results in production by utilising the scalability and flexibility of Azure in conjunction with the highly effective and performant language models from Cohere.

With Cohere Rerank, Atomicwork, a digital workplace experience platform and a seasoned Azure user, has greatly improved its IT service management platform. Atomicwork has enhanced search relevancy and accuracy by incorporating the model into Atom AI, their AI digital assistant, hence offering quicker, more accurate responses to intricate IT help enquiries. Enterprise-wide productivity has increased as a result of this integration, which has simplified IT processes.

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[govindhtech]

GPT-4o Mini: OpenAI’s Most Cost-Efficient Small Model

OpenAI is dedicated to maximising the accessibility of intelligence. OpenAI is pleased to present the GPT-4o mini, their most affordable little variant. Because GPT-4o mini makes intelligence considerably more affordable, OpenAI anticipate that it will greatly increase the breadth of applications produced using  AI. GPT-4o mini beats GPT-4 on conversation preferences in the LMSYS leaderboard, scoring 82% on MMLU at the moment (opens in a new window). It is priced at 15 cents per million input tokens and 60 cents per million output tokens, which is more than 60% less than GPT-3.5 Turbo and an order of magnitude more affordable than prior frontier models.

GPT-4o mini’s low cost and latency enable a wide range of applications, including those that call multiple APIs, chain or parallelize multiple model calls, pass a large amount of context to the model (such as the entire code base or conversation history), or engage with customers via quick, real-time text responses (e.g., customer support chatbots).

The GPT-4o mini currently supports text and vision inputs and outputs through the API; support for image, text, video, and audio inputs and outputs will be added later. The model supports up to 16K output tokens per request, has a context window of 128K tokens, and has knowledge through October 2023. It is now even more economical to handle non-English text because of the enhanced tokenizer that GPT-4o shared.

A little model with superior multimodal reasoning and textual intelligence

GPT-4o mini supports the same range of languages as GPT-4o and outperforms GPT-3.5 Turbo and other small models on academic benchmarks in textual intelligence and multimodal reasoning. Additionally, it shows better long-context performance than GPT-3.5 Turbo and excellent function calling speed, allowing developers to create applications that retrieve data or interact with external systems.

GPT-4o mini has been assessed using a number of important benchmarks.

Tasks incorporating both text and vision reasoning: GPT-4o mini outperforms other compact models with a score of 82.0% on MMLU, a benchmark for textual intelligence and reasoning, compared to 77.9% for Gemini Flash and 73.8% for Claude Haiku.

Proficiency in math and coding: The GPT-4o mini outperforms earlier tiny models available on the market in activities including mathematical reasoning and coding. GPT-4o mini earned 87.0% on the MGSM, a test of math thinking, compared to 75.5% for Gemini Flash and 71.7% for Claude Haiku. In terms of coding performance, GPT-4o mini scored 87.2% on HumanEval, while Gemini Flash and Claude Haiku scored 71.5% and 75.9%, respectively.

Multimodal reasoning: The GPT-4o mini scored 59.4% on the Multimodal Reasoning Measure (MMMU), as opposed to 56.1% for Gemini Flash and 50.2% for Claude Haiku, demonstrating good performance in this domain.

OpenAI collaborated with a few reliable partners as part of their model building approach to gain a deeper understanding of the capabilities and constraints of GPT-4o mini. Companies like Ramp(opens in a new window) and Superhuman(opens in a new window), with whom they collaborated, discovered that GPT-4o mini outperformed GPT-3.5 Turbo in tasks like extracting structured data from receipt files and producing excellent email responses when given thread history.

Integrated safety precautions

OpenAI models are constructed with safety in mind from the start, and it is reinforced at every stage of the development process. Pre-training involves filtering out (opens in a new window) content that they do not want their models to encounter or produce, including spam, hate speech, adult content, and websites that primarily collect personal data. In order to increase the precision and dependability of the models’ answers, OpenAI use post-training approaches like reinforcement learning with human feedback (RLHF) to align the model’s behaviour to their policies.

The safety mitigations that GPT-4o mini has in place are identical to those of GPT-4o, which they thoroughly examined using both automated and human reviews in accordance with their preparedness framework and their voluntary commitments. OpenAI tested GPT-4o with over 70 outside experts in social psychology and disinformation to find potential dangers. OpenAI have resolved these risks and will provide more information in the upcoming GPT-4o system card and Preparedness scorecard. These expert assessments have yielded insights that have enhanced the safety of GPT-4o and GPT-4o mini.

Based on these discoveries, OpenAI groups additionally sought to enhance GPT-4o mini’s safety by implementing fresh methods that were influenced by their study. The first model to use their instruction hierarchy(opens in a new window) technique is the GPT-4o mini in the API. This technique helps to strengthen the model’s defence against system prompt extractions, jailbreaks, and prompt injections. As a result, the model responds more consistently and is safer to use in large-scale applications.

As new hazards are discovered, OpenAI will keep an eye on how GPT-4o mini is being used and work to make the model safer.

Accessibility and cost

As a text and vision model, GPT-4o mini is now accessible through the Assistants API, Chat Completions API, and Batch API. The cost to developers is 15 cents for every 1 million input tokens and 60 cents for every 1 million output tokens, or around 2500 pages in a typical book. In the upcoming days, OpenAI want to launch GPT-4o mini fine-tuning.

GPT-3.5 will no longer be available to Free, Plus, and Team users in ChatGPT; instead, they will be able to access GPT-4o mini. In keeping with OpenAI goal of ensuring that everyone can benefit from  artificial intelligence, enterprise users will also have access starting next week.

Next Steps

In recent years, there have been notable breakthroughs in  artificial intelligence along with significant cost savings. For instance, since the introduction of the less capable text-davinci-003 model in 2022, the cost per token of the GPT-4o mini has decreased by 99%. OpenAI is determined to keep cutting expenses and improving model capabilities in this direction.

In the future, models should be readily included into all websites and applications. Developers may now more effectively and economically create and expand robust  AI applications thanks to GPT-4o mini. OpenAI is thrilled to be leading the way as  AI becomes more dependable, approachable, and integrated into their everyday digital interactions.

Azure AI now offers GPT-4o mini, the fastest model from OpenAI

Customers can deliver beautiful apps at a reduced cost and with lightning speed thanks to GPT-4o mini. GPT-4o mini is more than 60% less expensive and considerably smarter than GPT-3.5 Turbo, earning 82% on Measuring Massive Multitask Language Understanding (MMLU) as opposed to 70%.1. Global languages now have higher quality thanks to the model’s integration of GPT-4o’s enhanced multilingual capabilities and larger 128K context window.

The OpenAI-announced GPT-4o mini is available concurrently on Azure AI, offering text processing at a very high speed; picture, audio, and video processing to follow. Visit the Azure OpenAI Studio Playground to give it a try for free.

GPT-4o mini is safer by default thanks to Azure AI

As always, safety is critical to the efficient use and confidence that Azure clients and Azure both need.

Azure is happy to report that you may now use GPT-4o mini on Azure OpenAI Service with their Azure  AI Content Safety capabilities, which include protected material identification and prompt shields, “on by default.”

To enable you to take full advantage of the advances in model speed without sacrificing safety, Azure has made significant investments in enhancing the throughput and performance of the Azure  AI Content Safety capabilities. One such investment is the addition of an asynchronous filter. Developers in a variety of industries, such as game creation (Unity), tax preparation (H&R Block), and education (South Australia Department for Education), are already receiving support from Azure AI Content Safety to secure their generative AI applications.

Data residency is now available for all 27 locations with Azure AI

Azure’s data residency commitments have applied to Azure OpenAI Service since the beginning.

Azure AI provides a comprehensive data residency solution that helps customers satisfy their specific compliance requirements by giving them flexibility and control over where their data is processed and kept. Azure also give you the option to select the hosting structure that satisfies your needs in terms of applications, business, and compliance. Provisioned Throughput Units (PTUs) and regional pay-as-you-go provide control over data processing and storage.

Azure is happy to announce that the Azure OpenAI Service is currently accessible in 27 locations, with Spain being the ninth region in Europe to launch earlier this month.

Global pay-as-you-go with the maximum throughput limitations for GPT-4o mini is announced by Azure AI

With Azure’s global pay-as-you-go deployment, GPT-4o mini is now accessible for 15 cents per million input tokens and 60 cents per million output tokens, a substantial savings over earlier frontier models.

Customers can enjoy the largest possible scale with global pay-as-you-go, which offers 30 million tokens per minute (TPM) for GPT-4o and 15 million TPM (TPM) for GPT-4o mini. With the same industry-leading speed and 99.99% availability as their partner OpenAI, Azure OpenAI Service provides GPT-4o mini.

For GPT-4o mini, Azure AI provides industry-leading performance and flexibility

Azure  AI is keeping up its investment in improving workload efficiency for  AI across the Azure OpenAI Service.

This month, GPT-4o mini becomes available on their Batch service in Azure AI. By utilising off-peak capacity, Batch provides high throughput projects with a 24-hour turnaround at a 50% discount rate. This is only feasible because Microsoft is powered by Azure AI, which enables us to provide customers with off-peak capacity.

This month, Azure is also offering GPT-4o micro fine-tuning, which enables users to further tailor the model for your unique use case and situation in order to deliver outstanding quality and value at previously unheard-of speeds. Azure lowered the hosting costs by as much as 43% in response to their announcement last month that Azure would be moving to token-based charging for training. When combined with their affordable inferencing rate, Azure OpenAI Service fine-tuned deployments are the most economical option for clients with production workloads.

Azure is thrilled to witness the innovation from businesses like Vodafone (customer agent solution), the University of Sydney ( AI assistants), and GigXR ( AI virtual patients), with more than 53,000 customers turning to Azure AI to provide ground-breaking experiences at incredible scale. Using Azure OpenAI Service, more than half of the Fortune 500 are developing their apps.

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[govindhtech]

Announcing GPT-4o: OpenAI’s new flagship model on Azure AI

Today, ChatGPT is beginning to push out GPT-4o’s text and image capabilities. OpenAI is launching GPT-4o in the free tier and offering up to five times higher message limits to Plus customers. In the upcoming weeks, ChatGPT Plus will launch an early version of a new Voice Mode that integrates GPT-4o.

GPT-4, OpenAI’s newest deep learning scaling milestone. GPT-4 is a large multimodal model that handles image and text inputs and outputs text. While less proficient than humans in many real-world situations, it performs at human levels on professional and academic benchmarks. It scores in the top 10% of simulated bar exam takers, while GPT-3.5 scores in the bottom 10%. After six months of progressively aligning GPT-4 utilising learning from our adversarial testing programme and ChatGPT, OpenAI achieved their best-ever results on factuality, steerability, and guardrail refusal.

Over two years, OpenAI updated their deep learning stack and co-designed a supercomputer with Azure for their workload. For the system’s first “test run,” OpenAI trained GPT-3.5 last year. Some flaws were resolved and their theoretical underpinnings enhanced. Thus, OpenAI’s GPT-4 training run was unprecedentedly steady, becoming OpenAI’s first huge model whose training performance OpenAI could precisely anticipate. As OpenAI focus on dependable scalability, OpenAI want to improve our technique to foresee and plan for future capabilities earlier, which is crucial for safety.

GPT-4 text input is coming to ChatGPT and the API (with a waiting).OpenAI is working with one partner to make picture input available to more people. OpenAI also open-sourcing OpenAI Evals, their platform for automatic AI model performance review, so anyone may report model flaws to help us improve.

Capabilities

With its ability to receive any combination of text, audio, and image as input and produce any combination of text, audio, and image outputs, GPT-4o (o stands for “omni”) is a step towards far more natural human-computer interaction. It has a response time of up to 320 milliseconds on average while responding to audio inputs, which is comparable to a human’s response time(opens in a new window) during a conversation. It is 50% less expensive and significantly faster in the API, and it matches GPT-4 Turbo speed on text in non-English languages while maintaining performance on text in English and code. When compared to other models, it excels particularly at visual and audio understanding.

You could speak with ChatGPT using Voice Mode with average latency of 2.8 seconds (GPT-3.5) and 5.4 seconds (GPT-4) before GPT-4o. Voice Mode does this by using a pipeline made up of three different models: GPT-3.5 or GPT-4 takes in text and outputs text, a third basic model translates that text back to audio, and a simple model transcribes audio to text. The primary source of intelligence, GPT-4, loses a lot of information as a result of this process. It is unable to directly perceive tone, numerous speakers, background noise, or laughter or emotion expression.

By using it, OpenAI were able to train a single new model end-to-end for text, vision, and audio, which means that the same neural network handles all inputs and outputs. Since GPT-4o is their first model to incorporate all of these modalities, OpenAI have only begun to explore the capabilities and constraints of the model.

Evaluations of models

It surpasses previous standards in terms of multilingual, audio, and visual capabilities, while achieving GPT-4 Turbo-level performance in terms of text, reasoning, and coding intelligence.

Tokenization of language

These 20 languages were selected to serve as an example of how the new tokenizer compresses data across various language families.

Gujarati 4.4x fewer tokens (from 145 to 33)

હેલો, મ��રું નામ જીપીટી-4o છે. હું એક નવા પ્રકારનું ભાષા મોડલ છું. તમને મળીને સારું લાગ્યું!

Telugu 3.5x fewer tokens (from 159 to 45)

నమస్కారము, నా పేరు జీపీటీ-4o. నేను ఒక్క కొత్త రకమైన భాషా మోడల్ ని. మిమ్మల్ని కలిసినందుకు సంతోషం!

Tamil 3.3x fewer tokens (from 116 to 35)

வணக்கம், என் பெயர் ஜிபிடி-4o. நான் ஒரு புதிய வகை மொழி மாடல். உங்களை சந்தித்ததில் மகிழ்ச்சி!

Marathi 2.9x fewer tokens (from 96 to 33)

नमस्कार, माझे नाव जीपीटी-4o आहे| मी एक नवीन प्रकारची भाषा मॉडेल आहे| तुम्हाला भेटून आनंद झाला!

Hindi 2.9x fewer tokens (from 90 to 31)

नमस्ते, मेरा नाम जीपीटी-4o है। मैं एक नए प्रकार का भाषा मॉडल हूँ। आपसे मिलकर अच्छा लगा!

Urdu 2.5x fewer tokens (from 82 to 33)

ہیلو، میرا نام جی پی ٹی-4o ہے۔ میں ایک نئے قسم کا زبان ماڈل ہوں، آپ سے مل کر اچھا لگا!

Arabic 2.0x fewer tokens (from 53 to 26)

مرحبًا، اسمي جي بي تي-4o. أنا نوع جديد من نموذج اللغة، سررت بلقائك!

Persian 1.9x fewer tokens (from 61 to 32)

سلام، اسم من جی پی تی-۴او است. من یک نوع جدیدی از مدل زبانی هستم، از ملاقات شما خوشبختم!

Russian 1.7x fewer tokens (from 39 to 23)

Привет, меня зовут GPT-4o. Я — новая языковая модель, приятно познакомиться!

Korean 1.7x fewer tokens (from 45 to 27)

안녕하세요, 제 이름은 GPT-4o입니다. 저는 새로운 유형의 언어 모델입니다, 만나서 반갑습니다!

Vietnamese 1.5x fewer tokens (from 46 to 30)

Xin chào, tên tôi là GPT-4o. Tôi là một loại mô hình ngôn ngữ mới, rất vui được gặp bạn!

Chinese 1.4x fewer tokens (from 34 to 24)

你好，我的名字是GPT-4o。我是一种新型的语言模型，很高兴见到你!

Japanese 1.4x fewer tokens (from 37 to 26)

こんにちわ、私の名前はGPT−４oです。私は新しいタイプの言語モデルです、初めまして

Turkish 1.3x fewer tokens (from 39 to 30)

Merhaba, benim adım GPT-4o. Ben yeni bir dil modeli türüyüm, tanıştığımıza memnun oldum!

Italian 1.2x fewer tokens (from 34 to 28)

Ciao, mi chiamo GPT-4o. Sono un nuovo tipo di modello linguistico, è un piacere conoscerti!

German 1.2x fewer tokens (from 34 to 29)

Hallo, mein Name is GPT-4o. Ich bin ein neues KI-Sprachmodell. Es ist schön, dich kennenzulernen.

Spanish 1.1x fewer tokens (from 29 to 26)

Hola, me llamo GPT-4o. Soy un nuevo tipo de modelo de lenguaje, ¡es un placer conocerte!

Portuguese 1.1x fewer tokens (from 30 to 27)

Olá, meu nome é GPT-4o. Sou um novo tipo de modelo de linguagem, é um prazer conhecê-lo!

French 1.1x fewer tokens (from 31 to 28)

Bonjour, je m’appelle GPT-4o. Je suis un nouveau type de modèle de langage, c’est un plaisir de vous rencontrer!

English 1.1x fewer tokens (from 27 to 24)

Hello, my name is GPT-4o. I’m a new type of language model, it’s nice to meet you!

Availability of the model

OpenAI’s most recent endeavour to expand the capabilities of deep learning this time towards usefulness in real-world applications is GPT-4o. Over the past two years, they have put a lot of effort into increasing efficiency at every stack layer. OpenAI are able to provide a GPT-4 level model to a much wider audience as a first fruit of this study. Iteratively, the capabilities of GPT-4o will be released (with enhanced red team access commencing immediately).

The API lets developers use GPT-4o for text and vision. Compared to GPT-4 Turbo, GPT-4o has five times higher rate limitations, is half the price, and is two times faster. In the upcoming weeks, OpenAI intend to make support for GPT-4o’s enhanced audio and video capabilities available via the API to a select number of reliable partners.

OpenAI, known for ChatGPT, has advanced huge language models with GPT-4o. Multimodal processing and response to text, visuals, and audio make it stand out. The salient characteristics of GPT-4o are as follows:

Essential features:

Multimodal: This is GPT-4o‘s most important feature. It is capable of processing and reacting to audio, pictures, and text. Consider giving it an audio clip and asking it to summarise the conversation, or showing it a picture and asking it to compose a poem about it.

Enhanced performance: According to OpenAI, GPT-4o performs better than its predecessors in a number of domains, including text production, audio processing, image identification, and complicated text interpretation. Limitations and safety:

Focus on safety: By screening training data and putting safety measures in place, OpenAI puts safety first. Additionally, in order to find any potential problems like bias or manipulation, they have carried out risk assessments and external testing.

Restricted distribution: Currently, GPT-4o’s text and image input/output features are accessible via OpenAI’s API. There may be a subsequent release with audio capability.

Concerns

Particular skills: It’s uncertain how much GPT-4o can really do when it comes to multimodal reasoning or complicated audio problems.

Long-term effects: It’s too soon to say what practical uses and possible downsides GPT-4o may have.

With great pleasure, Microsoft announces the release of OpenAI’s new flagship model, GPT-4o, on Azure AI. This innovative multimodal model raises the bar for conversational and creative AI experiences by combining text, visual, and audio capabilities. GPT-4o is currently available for preview in the Azure OpenAI Service and supports both text and images.

A breakthrough for Azure OpenAI Service’s generative AI

A change in the way AI models engage with multimodal inputs is provided by GPT-4o. Through the seamless integration of text, graphics, and music, GPT-4o offers a more immersive and dynamic user experience.

Highlights of the launch: Quick access and what to anticipate

Customers of Azure OpenAI Service can now, in two US locations, explore the vast potential of GPT-4o via a preview playground in Azure OpenAI Studio. The model’s potential is shown by this first version, which focuses on text and visual inputs, opening the door for additional features like audio and video.

Effectiveness and economy of scale

The GPT-4o is designed with efficiency and speed in mind. Its sophisticated capacity to manage intricate queries with less resources can result in improved performance and cost savings.

Possible applications to investigate using GPT-4o

The implementation of GPT-4o presents a multitude of opportunities for enterprises across diverse industries:

Improved customer service: GPT-4o allows for more dynamic and thorough customer assistance conversations by incorporating various data inputs.

Advanced analytics: Make use of GPT-4o’s capacity to handle and examine various data kinds in order to improve decision-making and unearth more profound insights.

Content innovation: Create interesting and varied content forms that appeal to a wide range of customer tastes by utilising GPT-4o’s generating capabilities.

Future advancements to look forward to: GPT-4o at Microsoft Build 2024

To assist developers in fully realising the potential of generative AI, Azure is excited to provide additional information about GPT-4o and other Azure AI advancements at Microsoft Build 2024.

Utilise Azure OpenAI Service to get started

Take the following actions to start using GPT-4o and Azure OpenAI Service:

Check out GPT-4o in the preview version of the Azure OpenAI Service Chat Playground.

If you don’t currently have access to Azure OpenAI Services, fill out this form to request access.

Find out more about the most recent improvements to the Azure OpenAI Service.

Learn about Azure’s responsible AI tooling with Azure AI Content Safety.

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11x Faster Search for Generative AI with Azure AI Search

Azure AI Search

In order to assist customers developing generative AI applications that are ready for production, Azure is pleased to announce today some major updates to Azure AI Search. In order to enable customers to run retrieval augmented generation (RAG) at any scale without sacrificing cost or performance, Azure AI Search has significantly increased storage capacity and vector index size at no additional cost.

This article will explain how clients can

Can use today’s changes to achieve more scalability at a lower cost.

Put their large RAG workloads in the hands of Azure AI Search.

To innovate in previously unthinkable ways, use sophisticated search techniques to navigate complex data.

Introducing Azure AI Search, which offers greater performance and scalability at a cheaper price

With much increased vector and storage capacity, Azure AI Search can now provide users with higher scalability, better performance, and more data for their money.

In some regions, the Basic and Standard tiers of Azure AI Search now have more available capacity and compute.

Users are going to see up to a

11 times larger vector index.

A sixfold rise in overall storage.

Indexing and query throughput improvements of two times.

With these adjustments, clients can provide excellent user and interaction experiences at any size. With just one search instance, users can scale their generative AI applications to a multi-billion vector index without sacrificing efficiency or speed.

Providing a reliable enterprise retrieval system to support sizable RAG-based applications

For the management of their mission-critical enterprise search and generative AI applications, more than half of Fortune 500 companies rely on Azure AI Search. Azure AI Search is used by OpenAI, Otto Group, KPMG, and PETRONAS to support workloads related to retrieval augmented generation (RAG).

OpenAI had to make sure their retrieval system could handle previously unheard-of demand and scale when they unveiled their Assistant API and RAG-powered “GPTs” at OpenAI DevDay 2023. Because of Azure AI Search’s ability to handle their massive, internet-scale RAG workloads, OpenAI turned to it.

Azure AI Search now offers search functionality to products such as the GPT Store and supports RAG capabilities for ChatGPT, GPTs, and the Assistant API. Azure AI Search is the retrieval system that makes these products function whenever someone searches within them or adds a file to them.

As of November 2023, 100 million people visit ChatGPT alone each week, and over 2 million developers use its API to build applications. Three million custom GPTs were generated in the first two months after their announcement. With users from all over the world, these are enormous numbers. Really RAG on a large scale.

Using a cutting-edge, modern retrieval system to create better applications

Using just one search technique, such as vector search, is ineffective for creating generative AI applications that function as intended, as teams in the professional services, healthcare, and telecommunications industries have discovered.

Certain use cases are better served by different retrieval strategies. To cover the range of scenarios that any given application is likely to encounter, high-quality retrieval systems combine multiple techniques.

Developers can accomplish goals more quickly and efficiently by using Azure AI Search to enable applications to apply a range of strategies straight out of the box, such as hybrid retrieval and semantic reranking.

Advanced RAG is used by Telus Health to provide a customer support application

Telus Health is a Canadian-based company that leads the way in offering technology-based services and solutions to insurers, individuals, employers, and healthcare professionals. In order to address user questions regarding particular health plans and provide assistance with using their website, the company launched a customer support platform. All of the requirements could not be met by the first implementation, which was based only on vector search. Telus Health resorted to Azure AI Search as a result, which is renowned for its cutting-edge, extensive suite of search technologies.

The Guide Team at Telus Health played a key role in developing their search approach and making efficient use of AI Search to improve the platform. Telus Health made it possible for the system to effectively handle queries pertaining to client-specific documents as well as those utilising the company website by broadening their retrieval strategy and introducing hybrid search with semantic reranking. This strategic improvement, made possible by Azure AI Search, has greatly increased the platform’s accuracy and responsiveness and demonstrates Telus Health’s dedication to providing top-notch customer service.

NIQ Brandbank uses multi-vector retrieval to enable brands to maximise their online presence

Fast-moving consumer goods (FMCG) brands can outperform their rivals by using NIQ Brandbank’s solutions to provide rich, pertinent content and imagery for their digital shelf.

With data-driven, practical advice and insights that demonstrate how their product content compares to competitors in the market, NIQ Brandbank’s Content Health+ solution enables brands to maximise their online presence.

The application helps brands increase sales, improve product placement across retailer search results, and improve their online presence with its straightforward, user-friendly format.

In order to determine which product attributes affect organic placement on the digital shelf, Content Health+ draws from research conducted by an NIQ Data Impact team. The application uses multi-vector search on the backend to search the research that is stored in both text and images. Search reranking is used to present the most relevant results. This feature makes excellent recommendations about the kind of content that a brand should prioritise in order to boost sales and performance.

Content Health+ was developed using hybrid multi-vector search and semantic ranking to ensure that the application functions as intended. Combining different retrieval techniques allows more ideas and opportunities to be realised for e-commerce and recommendation apps.

Find out more about Azure AI Search

They are facilitating the AI systems’ ability to retrieve information at scale by making these announcements today. With Azure AI Search’s cutting-edge retrieval technology and an enterprise-ready foundation, customers can innovate with confidence.

Leading search and information retrieval platform for RAG Azure AI Search, an AI-powered platform for information retrieval, assists developers in creating generative AI apps and rich search experiences by fusing enterprise data with large language models. Provide search capabilities for all mobile applications, internal search applications, and software as a service (SaaS) apps.

Simplify the creation and provision of search solutions

Simplify the process of creating search indexes and ingesting data by integrating them with Azure storage solutions, RESTful APIs, and SDKs. Implement a search service that is fully configured and offers user-friendly features like synonyms, faceting, scoring, and geo-search. Steer clear of the operational costs associated with debugging index corruption, keeping an eye on service availability, or manually scaling during traffic spikes.

Showcase the most pertinent outcomes for your users

Utilise cutting-edge deep learning models from Bing and Microsoft Research to give your apps results that are pertinent and contextual. Use the semantic search feature to provide customers with substantially better results, gain a deeper understanding of their search terms, and increase customer engagement. Knowledge mining and summary results are also made possible by semantic search, providing your users with quick snippets without making them scroll through a tonne of results.

Use Azure OpenAI Service to develop apps for the next generation

To apply the most sophisticated AI language models to your search solutions that use your own data as the foundation for responses, combine Azure AI Search with Azure OpenAI Service. ChatGPT, an Azure OpenAI service, allows you to retrieve enterprise data from knowledge bases using conversational language.

Adapt search features with AI integrations

Customise the search process to fit your organization’s particular needs. Key phrase extraction, language detection, optical character recognition (OCR), image analysis, translation, and role-based access control (RBAC) are just a few of the customizable features that Azure AI Search provides. Utilize the integration features offered by Azure AI services, such as Speech, Vision, Language, and Azure OpenAI Service, to enhance the conversion of unstructured, raw data into searchable content.

Scale to handle heavy traffic loads and big datasets

Easily index and search through enormous volumes of data, regardless of the size of your company, to provide excellent search results for your users without worrying about infrastructure management. Your search solution will be scalable as your company expands thanks to Azure AI Search’s ability to manage massive data loads and high traffic loads.

Use AI sensibly

With Azure AI Search, you can get access to cloud search tools, guidelines, and other resources to assist you in developing a responsible AI solution. Go through Microsoft’s responsible AI guidelines.

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Latest Marvels : Azure AI Data & Digital Apps Advancements

Azure AI Data, AI, and Digital Apps updates:

Modernize data, build smart apps, and use AI

Generational AI models and tools have improved application and business process experiences for years, but this year was a turning point.  Within months, customers and partners integrated AI into their transformation roadmaps and launched AI-powered Digital Apps and services.

A new technology has never caused such rapid change. It shows how many organizations were AI-ready and how cloud, data, DevOps, and transformation cultures prepared them. Customers and partners can maximize AI with hundreds of Microsoft resources, models, services, and tools this year.

New models and multimodal capabilities in Azure AI

They offer the most advanced open and frontier models so developers can build confidently and unlock immediate value across their organization. 

They added Models as a Service to Azure OpenAI Service last month. Azure AI applications can use model providers’ latest open and frontier LLMs.

MaaS for Llama 2 was announced last week. The ready-to-use API and token-based billing of MaaS for Llama 2 let developers integrate with their favorite LLM tools like Prompt Flow, Semantic Kernel, and LangChain. Hosted fine-tuning lets generative AI developers use Llama 2 without GPUs, simplifying model setup and deployment. Llama 2, purchased and hosted on Azure Marketplace, lets them sell custom apps. In his blog post, John Montgomery describes this announcement and shows Azure AI Model Catalog models. 

Here are improving Azure OpenAI Service and launched multimodal AI to let businesses build

Generative AI experiences with image, text, and video:

Preview DALL·E 3: Generate images from text descriptions. The AI model DALL·E 3 excels in this regard. DALL·E 3 generates images from user descriptions.

General availability: GPT-3.5 Turbo preview at

16k token prompt:

GPT-4 Turbo: Azure OpenAI Service models now extend prompt length and improve generative AI application control and efficiency.

Visionary GPT-4 Turbo preview: GPT-4V optimises experiences by generating text output from images or videos using Azure AI Vision enhancements like video analysis.

Modifying Azure OpenAI Service models: Fine-tune Azure OpenAI Service models Babbage-002, Davinci-002, and GPT-35-Turbo. Developers and data scientists can customize Azure OpenAI Service models. Discover fine-tuning.

GPT-4 updates: Azure OpenAI Service can fine-tune GPT-4. Organizations can customize the AI model by fine-tuning. It’s like customizing an AI suit. Checking GPT-4 updates.

Frontier steering: Prompting power grows

GPT-4 prompting dazzles! Microsoft Research recently blogged about promptbase, a reasoning-based GPT-4 prompt. Other AI models lag behind GPT-4 in various test sets, including those used to benchmark the recently announced Gemini Ultra. Zero-shot chain-of-thought prompting. See the blog post and try these GitHub resources.

LLMOps RAI tools and practices

Safety boundaries supporting short- and long-term ROI must be considered as AI adoption matures and companies produce AI apps. This month’s LLMOps for business leaders article covered integrating responsible AI into your AI development lifecycle. These Azure AI Studio best practices and tools help development teams apply their principles.

AI Advantage from Azure

Cloud database Azure Cosmos DB uses AI. Built-in AI, natural language queries, vector search, and simple Azure AI Search integration are supported. To demonstrate these benefits, her new Azure AI Advantage offer gives new and existing Azure AI and GitHub Copilot customers 40,000 RU/s of Azure Cosmos DB for 90 days.

Modern data and analytics platforms are essential for AI transformation because intelligent apps need data.

Multinational law firm Clifford Chance benefits clients with new technology. The company built a solid Azure data platform to test Azure AI, Microsoft 365 Copilot, and large language models. Cognitive translation is an IT team’s fastest-growing product.

Azure Machine Learning and Azure Databricks helped Belgian insurance company Belfius reduce development time, efficiency, and reliability. Data scientists can create and transform features while the company improves fraud and money laundering detection.

Co-innovation Azure-Databricks improves AI experiences

Customers and partners shared how maturing AI tools and services are helping them achieve more at Microsoft Ignite 2023 in November.

One of her strategic partners, Databricks offers Azure’s fastest-growing data services. Azure Databricks’ interoperability with Microsoft Fabric and use of Azure OpenAI to improve customer AI experiences were recently demonstrated. Customers can build retrieval-augmented generation (RAG) applications on Azure Databricks and analyze the output with Power BI in Fabric using Azure OpenAI LLMs.

Azure Database PostgreSQL AI extension

With the new Azure AI extension, Azure OpenAI LLMs can generate vector embeddings and build rich PostgreSQL generative AI applications. These powerful new capabilities and pgvector support make Azure Database for PostgreSQL another great place to build AI-powered apps.

SQL Server anywhere gets Azure Arc cloud innovation

SQL Server manageability and security improvements from Azure Arc are available this month. Customers can optimize database performance and gain critical SQL Server estate insights with SQL Server monitoring. Azure portal makes Always On availability groups, failover cluster instances, and backups more visible and simple.

Lower Azure SQL Database prices Calculate hyperscale

The new Azure SQL Database price Hyperscale gives cloud-native workloads Azure SQL performance and security at commercial open-source database prices. For scalable, AI-ready cloud applications of any size and I/O, hyperscale customers can save 35% on compute resources.

Apps change operations and experiences

Personalized employee apps and customer chatbots are examples of digital applications developed and deployed by companies using AI to improve operations and experiences. Updates like these enable innovation.

Custom copilots and the seven AI development pillars

Copilots are exciting, and Azure AI Studio in public preview lets developers build generative AI apps. Businesses must carefully design a durable, adaptable, and effective approach for this new era. What can AI developers do for customer engagement? Consider these seven pillars for your custom copilot.

AKS is a top cloud-native intelligent app platform

AI and Kubernetes will influence app development. AI and cloud-native collaboration drives innovation at scale, with Azure Kubernetes Service (AKS) supporting compute-intensive workloads like AI and machine learning. Brendan Burn’s KubeCon blog describes how Microsoft builds and supports customer-beneficial open-source communities.

Azure enables unlimited innovation.

Her recent portfolio news, resources, and features, especially digital applications, have received great tech community and customer response.

Ignite’s Platform Engineering Guide is a hit, demonstrating demand for this training. 

Technology innovation in companies is crucial.  

Two recent customer stories caught my eye.

Modernizing LEGO House interactive experiences with Azure Kubernetes

Here helping The LEGO House in Denmark, the ultimate LEGO experience center for kids and adults, migrate custom-built interactive digital experiences from an aging on-prem data center to Microsoft Azure Kubernetes Service (AKS) to improve stability, security, and iteration and collaboration on new guest experiences. LEGO House updates experiences faster with this cloud move and guest feedback. The modernizing destination hopes to share knowledge and technology with LEGOLAND and brand retail stores.

Gluwa chose Azure for a reliable, scalable cloud solution to bring banking to emerging, underserved markets and close the financial gap.

An estimated 1.4 billion people struggle to get credit or personal and business loans in a country with limited financial infrastructure. Borderless financial technology from blockchain helps Gluwa with Creditcoin stand out. The Azure cloud supports it. Gluwa has a solid platform with her.NET framework, Azure Container Instances, AKS, Azure SQL, Azure Cosmos DB, and more. The business is more efficient due to reliable uptime, stable services, and rich product offerings.

CARIAD builds Volkswagen Group vehicle service platform with Azure and AKS

The Volkswagen Group’s software subsidiary CARIAD created the CARIAD Service Platform with Microsoft using Azure and AKS to provide automotive applications to Audi, Porsche, Volkswagen, Seat, and Skoda as the industry moved to software-defined vehicles. This platform lets CARIAD developers develop and service vehicle software, giving Volkswagen an edge in next-generation automotive mobility.

AKS and Azure Arc help DICK’S Sporting Goods provide omnichannel service

To create a more consistent, personalized customer experience across its 850 stores and online retail experience, DICK’S Sporting Goods envisioned a “one store” technology strategy to write, deploy, manage, and monitor its store software across all locations nationwide and reflect those experiences on its eCommerce DICK’S needed modularity, integration, and simplicity to integrate its public cloud and edge computing systems.

DICK’s Sporting Goods is using Azure Arc and Azure Kubernetes Service to migrate its on-premise infrastructure to Azure and create an adaptive cloud environment. The retailer can now easily deploy new apps to every store for ubiquity.

Performance and efficiency of Azure Cobalt for intelligent apps

Azure has hundreds of cloud-native and intelligent application performance services. Azure silicon performance and efficiency efforts have expanded. Azure Maia, her first custom AI accelerator series for cloud-based AI training and inference, and Azure Cobalt, her first Microsoft Cloud CPU, were launched recently.

Azure Arm chips perform 40% slower than Cobalt 100, the first 64-bit 128-core chip in the series, which runs Microsoft Teams and Azure SQL.

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