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manageditserviceslosangeles · 4 days

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Elevate Your Educational Institution with Managed IT Services from ECF Data Solutions!

At ECF Data Solutions, we specialize in tailored IT solutions designed specifically for the education sector. Safeguard student data and ensure seamless access to essential teaching and learning tools with our Managed IT Services. Let us handle the tech complexities while you prioritize your students' growth and success. Discover how ECF Services can assist you in optimizing your educational management:

Cloud management AI for Education Tools Managed IT Services Powered by Azure AI, our innovative solutions revolutionize education by providing personalized insights, optimizing administrative processes, and fostering inclusivity. Experience the transformative power of Azure AI Translator for language translation, Azure OpenAI Service for virtual learning assistants, and Azure AI Content Safety for content filtering. Learn more about our comprehensive IT solutions and propel your institution towards excellence with ECF Data Solutions!

#ManagedITServices #EducationTechnology #AzureAI #ECFDataSolutions

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govindhtech · 23 days

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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.

Read more on Govindhtech.com

#govindhtech #TELUS #LLM #generativeai #AzureAI #azureaisearch #chatgpt #AzureOpenAIService #news #technews #technologynews #technology #technologytrends

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pulsaris · 1 month

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Inteligência Artificial - Cooperação entre NVIDIA e Microsoft

A Microsoft e a NVIDIA anunciaram integrações importantes no campo da Inteligência Artificial generativa para empresas em todo o mundo. Saiba mais em: https://news.microsoft.com/2024/03/18/microsoft-and-nvidia-announce-major-integrations-to-accelerate-generative-ai-for-enterprises-everywhere/

______ Direitos de imagem: © Microsoft (via https://news.microsoft.com/)

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geethaasrinivasan · 2 months

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youtube

Azure AI Fundamentals ai-900 part4 practice question

#youtube #azureai #ai900 #microsoftcertification #practice #azureaifundamentals

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kittu800 · 2 months

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youtube

Microsoft Fabric: The Future of Enterprise Analytics.

Mode of Training: Online

Join us on WhatsApp: https://bit.ly/47eayBz

Watch Demo Video: https://youtu.be/Fpoe2YF_uW8?si=hTCKsNDgcfM-RoTh

Visit: https://visualpath.in/microsoft-fabric-online-training-hyderabad.html

To subscribe to the Visualpath channel & get regular updates on further courses: https://www.youtube.com/c/Visualpath

#Microsoft #fabric #Azure #microsoftazure #AzureAI #OnlineLearning #training #trendingcourses #handsonlearning #visualpathedu #softwaredevelopment #PowerBI #DataFactory #synapse #dataengineering #DataAnalytics #DataWarehouse #datascience #Youtube

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enoumen · 2 years

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Machine Learning For Dummies on iOs: https://apps.apple.com/us/app/machine-learning-for-dummies-p/id1610947211 Windows: https://www.microsoft.com/en-ca/p/machine-learning-for-dummies-ml-ai-ops-on-aws-azure-gcp/9p6f030tb0mt Web/Android: https://www.amazon.com/gp/product/B09TZ4H8V6 #MachineLearning #AI #ArtificialIntelligence #ML #MachineLearningForDummies #MLOPS #NLP #ComputerVision #AWSMachineLEarning #AzureAI #GCPML https://www.instagram.com/p/CejSHqnp0-3/?igshid=NGJjMDIxMWI=

#machinelearning #ai #artificialintelligence #ml #machinelearningfordummies #mlops #nlp #computervision #awsmachinelearning #azureai #gcpml

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datanewoil · 5 years

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Azure Functions for Python just went to GA. And it works great with Cognitive Search!!

#azureai #cloud #azure #microsoft

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phungthaihy · 4 years

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R Support in Azure Machine Learning http://ehelpdesk.tk/wp-content/uploads/2020/02/logo-header.png [ad_1] In this episode we will discuss ... #androiddevelopment #angular #azureai #azuremachinelearning #azureml #azuremlr #azuremlrsdk #c #css #dataanalysis #datascience #deeplearning #developer #development #docker #iosdevelopment #java #javascript #machinelearning #microsoft #microsoftai #node.js #python #react #unity #webdevelopment

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hiramfleitas · 4 years

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🌎 AI Bootcamp - Tomorrow in Fort Lauderdale!

Join me tomorrow at the Microsoft Fort Lauderdale office or at a location near you. #GlobalAIBootcamp 🤖 https://globalai.community/global-ai-bootcamp #SQLServer #MachineLearning #AzureAI

I’ll be speaking at Global AI Bootcamp this weekend in the Microsoft Fort Lauderdale office. My session will be at 1:30 pm in Room 3, tomorrow, Saturday. It’s free to attend.

I am very excited to cover new material on Spark and Azure Data Studio Machine Learning Services extension which is soon to-be-announced. I hope you get the chance to join me and come the event.

Repo

https://github.com/hflei…

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#Azure Data Studio #Global AI Bootcamp #Machine Learning #Sentiment Analysis #Spark #SQL Server

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govindhtech · 1 month

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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.