I have been involved in many Intune Autopilot deployments where we have technicians who complete the pre-deploy on computers from HP and Dell before delivery. This has worked well for many years, but lately, we have received complaints from users because the computer arrives with the wrong OS/Display language.

I am aware of the settings for region and keyboard I deployment profiles. I also know how to change the language with LIP in Win10 and PowerShell in Win11.

What we have discovered now but not yet verified is the following: A computer from, for example, HP is shipped from the factory with multiple languages of the OS, which comes up as a question BEFORE the normal first screen in OOBE for region and keyboard, and if you choose Swedish in our case, the computer’s language becomes Swedish after pre-deploy.

What we have noticed now is that if you connect a network cable before starting the computer, the language choice in the OS never shows up; it instead jumps directly to region and keyboard and has chosen English as the OS language. It doesn’t matter if you run pre-deploy or user driven deploy the display language choice are skipped and defaults to English.

To get the language choice back, you must run restore with F11, i.e., the manufacturer’s image, and then restart pre-deploy without the network cable connected  until you have pressed Win 5 times.

Yes,i know it is possible to change it afterwards with PowerShell in this case for Windows 11, but it takes time.

Has anyone else noticed the same effect with multi-language, pre-deploy, and network cable?

Or have i missed a major change in Intune or Windows?

​I have been involved in many Intune Autopilot deployments where we have technicians who complete the pre-deploy on computers from HP and Dell before delivery. This has worked well for many years, but lately, we have received complaints from users because the computer arrives with the wrong OS/Display language.  I am aware of the settings for region and keyboard I deployment profiles. I also know how to change the language with LIP in Win10 and PowerShell in Win11.  What we have discovered now but not yet verified is the following: A computer from, for example, HP is shipped from the factory with multiple languages of the OS, which comes up as a question BEFORE the normal first screen in OOBE for region and keyboard, and if you choose Swedish in our case, the computer’s language becomes Swedish after pre-deploy.  What we have noticed now is that if you connect a network cable before starting the computer, the language choice in the OS never shows up; it instead jumps directly to region and keyboard and has chosen English as the OS language. It doesn’t matter if you run pre-deploy or user driven deploy the display language choice are skipped and defaults to English. To get the language choice back, you must run restore with F11, i.e., the manufacturer’s image, and then restart pre-deploy without the network cable connected  until you have pressed Win 5 times.  Yes,i know it is possible to change it afterwards with PowerShell in this case for Windows 11, but it takes time.  Has anyone else noticed the same effect with multi-language, pre-deploy, and network cable? Or have i missed a major change in Intune or Windows?  Read More

​

Hello, 

From time to time new language Keyboard Layouts appear on my Windows 11. Please look at the attachment. I have set up 2 languages English United States and Greek, but now and then (!) ENG United Kingdom and ENG United States International appear on my system! If I restart they may disappear and come back another day!

Can you please help me out with this?

​Hello,  From time to time new language Keyboard Layouts appear on my Windows 11. Please look at the attachment. I have set up 2 languages English United States and Greek, but now and then (!) ENG United Kingdom and ENG United States International appear on my system! If I restart they may disappear and come back another day!Can you please help me out with this?   Read More

​

What do the users and RA account have to have if they are setup in a AA and call queue to be able to call out as the RA account phone number?  Is is possible for a user in a call queue to call out only as the RA phone number, ie they have no phone number tied to their personal account?  Also if there is anydocumentation from Microsoft that spells this out I would appreciate it.  I have found nothing on this topic. 

​What do the users and RA account have to have if they are setup in a AA and call queue to be able to call out as the RA account phone number?  Is is possible for a user in a call queue to call out only as the RA phone number, ie they have no phone number tied to their personal account?  Also if there is anydocumentation from Microsoft that spells this out I would appreciate it.  I have found nothing on this topic.     Read More

​

Are you ready to uncover game-changing solutions and explore the latest developments in AI technology, but can’t make it to Chicago for our in-person event this year? You can still join us to discover the new opportunities for your business at Microsoft Ignite online, which will take place between November 19-21.  

Join Microsoft experts, industry trendsetters, IT practitioners, and fellow business leaders—from anywhere in the world—by attending our virtual sessions with a no-cost online pass.  

When you register for a Microsoft Ignite online pass, you gain access to the keynote livestream, live-streamed sessions, on-demand content, explorations of the latest AI technology, and digital swag. You’ll also be able to engage with the online experience features, such as:  

On-demand keynotes and sessions 

Digital favorites 

Session scheduler  

Attendee and featured partner directory 

Depending on the topics you’re interested in, you can attend specific in-depth breakout sessions geared toward your needs. Browse a range of topics, from understanding the essentials of AI adoption to maximizing developer efficiency with GitHub Copilot. With a wide array of sessions to choose from, you can tap into multiple opportunities to grow your team’s skillset, build strong connections with other partners and potential customers, and help your organization gain a competitive edge in today’s changing tech landscape.  

Register now as a digital attendee to get your no-cost online pass. Once you’re registered, you can dig into the session catalog to check out what we have in store for you this year and get more details about the sessions. Throughout Microsoft Ignite, we’ll help your organization identify opportunities to achieve more with the power of the Microsoft Cloud, Microsoft Copilot, and AI. 

We’re excited to connect with you at Microsoft Ignite this year, where together we can build solutions that will help customers move forward with confidence. 

Register now!

​Microsoft Tech Community – Latest Blogs –Read More 

When I was first asked to think about what the Future of AI would look like for developers, my response was instinctive. It has to start with model choice! Today, I want to dive into this topic in more depth. This is the first of a multi-part series where I hope to take you from catalog to code to cloud, as we build intelligent applications on Azure AI.

🚀 | Want to learn more about model choice and related Azure AI platform tools & capabilities?

Read our monthly Hugging Face Models on Azure AI post. We review ~20 new models and surface interesting trends and uses.
Register for Microsoft Ignite (virtual or in-person). Keep an eye out for these sessions:

Azure AI: Effortless model selection – explore, swap and scale faster
AI Customization: Tailoring models for your enterprise (Breakout)
A journey into open-source Hugging Face models on Azure AI (Theater demo)

The Paradox of Choice

Language models are at the heart of generative AI applications. Our choice of model has consequences in the quality of our application responses and the costs of our solution development. Our decision can help us expand our reach to the edge (with small language models) or improve our precision for specialized applications (with fine-tuning). Choices make the difference between a prototype that has promise and a product that has scale. But making a model choice can be challenging.

The 2023 paper “Harnessing the Power Of LLMs in Practice: A Survey on ChatGPT and Beyond”) charted a fairly sparse LLM landscape with just a handful of model choices (like GPT-4, Llama and Claude) as shown in the figure below. This made it easy for us to get productive quickly with model features and APIs and select the best fit our scenario requirements.

Fast-forward to 2024 and we have 1M+ community-created variants on Hugging Face and a rapidly growing ecosystem of foundation models from providers like Cohere, Mistral, AI21 Labs, Jais, Nixtla, Google, and Microsoft. We have Small Language Models (SLM) extending AI app use to the edge, and Specialized Models (domain-specific) with capabilities ranging from multi-lingual (Jais) to time-series forecasting (Nixtla), healthcare, and more.

This is creating a paradox of choice where the abundance of options makes us afraid to commit to one for fear we may miss out on a better choice for our needs. How do we overcome our analysis paralysis? In this blog post, we’ll explore a 3-part solution that can help mitigate these challenges:

Discovery: Avoid decision fatigue. Use a structured process to shortlist a selection.
Assessment: Find the best fit. Use the right tools and metrics to assess the shortlist.
Development: Make switches easy. Use model-agnostic APIs when coding the app.

What Developers Need

Every generative AI application design architecture starts with the basic question: What models should I use to bring my scenario to life? As developers looking at the end-to-end application lifecycle, our model selection needs to three things into accounts:

Ideation – can I prototype my application scenario with this model?
Augmentation – can I optimize the model to improve response quality and safety?
Operationalization – can I scale usage of the model in production deployments?

The problem for us today is that the model ecosystem is fragmented. Foundation models are published to provider-hosted sites along with their docs & samples, while community-created variants are published to model hubs like Hugging Face. Developers get differing levels of information from each source. This means that the burden of discovery falls on the Developer, requiring them to visit different model playgrounds, use different SDKs, and figure out the right metrics to compare options before choosing one. 

This gives rise to decision fatigue where the ability to take a decision is hampered by the fear there may be a better option that is unknown to us. What we need is a structured process for model selection that helps filter out irrelevant options until we get a manageable subset that can be evaluated for specific application needs. A comprehensive platform, like Azure AI Studio (shown below), provides tools and support required to make the process seamless. Let’s dive in!

A Structured Process

Let’s revisit the 3-part solution we talked about before and understand the question we are trying to answer at each step, and the challenge we face.

Discovery is about finding all the possible options to choose from. This requires us first to know that a specific option exists in this growing ecosystem, then trust that it meets privacy, security and safety standards for enterprise use.
Assessment is about filtering the choices until there is a manageable shortlist to evaluate more carefully. This requires asking the right questions, preferably in the right order, to eliminate unacceptable choices. Identifying the right criteria for comparing the remaining model choices will help zoom in to make a final decision.
Assignment is about fitting that model into the application architecture for the scenario. By decoupling the model selection from the task-based application, Developers can swap models without having to re-write code. Then, the model should be tested, allowing for iteration to go from initial prompt to functional prototype.

A Seamless Platform

This is where having a comprehensive platform to streamline end-to-end workflow helps! The Azure AI platform offers a one-stop shop for going from model selection to managed solution, in a unified manner with rich tooling, turnkey services, and easy integrations. For our model selection needs, it provides three key features:

Azure AI model catalog –  with 1.7K+ enterprise-ready models from trusted partners
Azure AI model benchmarks – with metrics & dataset filters to compare model choices
Azure AI model inference API – with a task-specific, model-agnostic API to code against

These capabilities map directly to our needs for discovery, assessment & assignment

Discovery: “There’s a model For that!”

The Azure AI model catalog contains over 1.7K. models, carefully curated with the help of trusted partners and customer feedback, to support enterprise scenarios. We add ~20 community-created variants to the Hugging Face collection each month, reflecting growing interest in multi-lingual capabilities, small language models, and domain specialization. Have a specific criteria for your app scenario? There’s a model for that! 

Assessment: “There’s a metric for that!”

The Azure AI model benchmarks  help you assess model performance on quality metrics like groundedness, coherence, fluency and similarity across a curated list of foundation LLM and SLM models. Use accuracy scores at model and dataset levels to compare options, picking the criteria that is right for your scenario. Filter views by task or other criteria, for more effective comparisons. And, you can potentially write your own custom evaluators to score models for specific criteria. Want a way to pick the best fit from a shortlisted selection of models? There’s a metric for that!

Alignment: “There’s a mechanism for that!”

The Azure AI model inference API allows developers to work with diverse models and providers using a common syntax and language. This makes it easier to plug-in different models or swap them later. This layer of abstraction also has extensibility to support any unique model features. It works with a core subset of models today (in serverless API and managed compute) with more to come. Want to compare the different models using one  codebase? There’s a mechanism for that!

The Model Selection Process on Azure

In our catalog to code to cloud journey, we’ll focus on the discovery process today and revisit the assessment and assignment steps in future posts in this series. The illustrated guide below gives you the big picture for model selection, and highlights the steps we can use for shortlisting catalog options to get a manageable subset, in tiles 3-6. Click here to view a hi-res (downloadable) version of the image.

Start by having developers ask the right questions to reduce the catalog choices to a more manageable 1-3 options for assessment.

Task: What is the application’s main inference task? Selecting the right task can drastically cut down model options, making it the perfect first step in filtering. For example, there are many options for “text generation” or “question answering” but fewer options for “image generation”. Starting with this filter ensures you are focusing on the right subset to start with.
Specialization. Does the application require a higher precision for a specialized domain? Does a specialized model exist, or can one be trained with your data? The first requires searching by keywords or task taxonomy – while the second checks to see if fine-tuning models exist for the targeted task, or if there exists a community-created variant that meets your needs.
Performance. Does the application require a real-time response (e.g., mobile or edge devices) or is round-trip latency to cloud an acceptable tradeoff for other constraints? This can effectively become a choice between SLM and LLM options.
Constraints. Does the solution development have associated cost or resource constraints that impact the decision? Does serverless API deployment (pay-per-token) work better than managed compute (pay-per-VM) for current needs?

The Azure AI model catalog allows us to filter models by various criteria – including collection (provider), license (usage), deployment (payment options), task (taxonomy) and keyword (search). Results are laid out as model cards that provide more details like model weights, relevant samples, and any assessments or datasets used for fine-tuning that model (in the case of variants). Deploy a model from the card to get an active endpoint and a playground that you can use for code-first and low-code validation respectively. 

Want to do a more rigorous assessment? We’ll explore that in our next post, looking at using Azure AI model benchmarks and the Azure AI model inference API, with a practical application scenario. For now, check out this Microsoft Mechanics blog post on how to choose the right models for your apps or watch their 8-minute video walkthrough of the process linked below.

🚀 | Want to learn more about model choice and related Azure AI platform tools & capabilities?

Read our monthly Hugging Face Models on Azure AI post. We review ~20 new models and surface interesting trends and uses.
Register for Microsoft Ignite (virtual or in-person). Keep an eye out for these sessions:

Azure AI: Effortless model selection – explore, swap and scale faster
AI Customization: Tailoring models for your enterprise (Breakout)
A journey into open-source Hugging Face models on Azure AI (Theater demo)

​Microsoft Tech Community – Latest Blogs –Read More 

Is anyone using non-M365 connectors for their search at https://www.microsoft365.com/ or https://www.office.com/? 

We have a lot of content in WordPress, ServiceNow, Saba, etc…that we are trying to return in the search results for employees. Unless they use the connector source in the All Sources tab, the results are pages and pages of Microsoft content if you ever get to the connector content you are looking for.

Anyone else experiencing this?

​Is anyone using non-M365 connectors for their search at https://www.microsoft365.com/ or https://www.office.com/? We have a lot of content in WordPress, ServiceNow, Saba, etc…that we are trying to return in the search results for employees. Unless they use the connector source in the All Sources tab, the results are pages and pages of Microsoft content if you ever get to the connector content you are looking for. Anyone else experiencing this?  Read More

​

I’m running Excel 2021 on Windows 10 Pro (64-bit).
I seem to be having some form of delay with the application of Conditional Formatting,
where I have to scroll past the cells subject to the formatting, before the effect takes place.

Sheet2 looks like:

I want the conditional formatting to affect any cell in $B$6:$F$91 that matches
any of the numbers entered in $B$2:$F$2 by changing the font color to Red and Bold.

The rules manager shows the following:

If I enter a 4 in $D$2 and press enter, cell $D$6 does not change until I click in the window
outside the sheet, or scroll down so that row 6 disappears at the top of the screen.

Similarly, if I clear the contents of $C$2, the number 03 in row 6 stays red until I scroll
row 6 off the top of the screen.

I don’t know if I’ve missed some important aspect of conditional formatting or
if I’ve managed to affect some global setting in Excel.

Once I’ve figured out this problem, I want to display a count of the cells in each row
that were conditionally formatted, a 2 for example in $G$6.
Any help would be appreciated.

​I’m running Excel 2021 on Windows 10 Pro (64-bit).I seem to be having some form of delay with the application of Conditional Formatting,where I have to scroll past the cells subject to the formatting, before the effect takes place.Sheet2 looks like:I want the conditional formatting to affect any cell in $B$6:$F$91 that matchesany of the numbers entered in $B$2:$F$2 by changing the font color to Red and Bold.The rules manager shows the following:If I enter a 4 in $D$2 and press enter, cell $D$6 does not change until I click in the windowoutside the sheet, or scroll down so that row 6 disappears at the top of the screen.Similarly, if I clear the contents of $C$2, the number 03 in row 6 stays red until I scrollrow 6 off the top of the screen.I don’t know if I’ve missed some important aspect of conditional formatting or if I’ve managed to affect some global setting in Excel.Once I’ve figured out this problem, I want to display a count of the cells in each rowthat were conditionally formatted, a 2 for example in $G$6.Any help would be appreciated.  Read More

​

Akses kepada peranti Apple saya.

​ Akses kepada peranti Apple saya.   Read More

​

A new, public toolset for analyzing the performance of Windows / Office / Apps is now available on the Microsoft GitHub site: https://github.com/Microsoft/MSO-Scripts

Based on tools used by MS Office teams to promote broad use of Event Tracing for Windows (ETW), it’s now available to facilitate performance analysis by IT Pros, etc.

We’re looking for help to BETA test and review documentation.
Can you help?

The toolset consists of highly customizable PowerShell scripts & XML configs to drive WPR/WPA, plus a custom plug-in for network analysis.

Wiki documentation covers a wide variety of topics:

CPU/Thread activityNetwork connectionsFile and Disk I/OWindows Handles: Kernel, User, GDIMemory Usage: Heap, RAM, Working Set, Reference Set, …Office-specific loggingSymbol ResolutionCustom TracingCPU Counters,
etc.

There’s also a growing YouTube channel: Windows Performance Deep Dive
Introduction to MSO-Scripts (video)

Suggestions? Reports?

Thank you in advance…

​A new, public toolset for analyzing the performance of Windows / Office / Apps is now available on the Microsoft GitHub site: https://github.com/Microsoft/MSO-ScriptsBased on tools used by MS Office teams to promote broad use of Event Tracing for Windows (ETW), it’s now available to facilitate performance analysis by IT Pros, etc.We’re looking for help to BETA test and review documentation.Can you help? The toolset consists of highly customizable PowerShell scripts & XML configs to drive WPR/WPA, plus a custom plug-in for network analysis.Wiki documentation covers a wide variety of topics:CPU/Thread activityNetwork connectionsFile and Disk I/OWindows Handles: Kernel, User, GDIMemory Usage: Heap, RAM, Working Set, Reference Set, …Office-specific loggingSymbol ResolutionCustom TracingCPU Counters,etc.There’s also a growing YouTube channel: Windows Performance Deep DiveIntroduction to MSO-Scripts (video) Suggestions? Reports?Thank you in advance…  Read More

​

Simplifying Migration to Fabric Real-Time Intelligence for Power BI Real Time Reports

Power BI with real-time streaming has been the preferred solution for users to visualize streaming data. Real-time streaming in Power BI is being retired. We recommend users to start planning the migration of their data processing pipeline to Fabric Real-Time Intelligence.    

Microsoft Fabric Real-Time Intelligence is a part of the Microsoft Fabric platform. It enables businesses to ingest, process, analyze, and gain insights from real-time data streams. It provides an end-to-end solution for integrating real-time data pipelines, offering advanced analytics and data visualization in one place.

This document outlines the patterns and best practices as you explore Fabric Real-Time Intelligence and the Azure ecosystem.

1. Real-Time data flow architecture

Figure 1.1 below is a general data flow pattern for a real time data analysis and visualization on Azure.

Figure1.1

On the left side, we have common real time data ingestion service like Azure Event hub, IoTHub etc. Next, we have azure stream analytics which is a fully managed stream processing engine that is designed to analyze and process large volumes of streaming data with sub-millisecond latencies. You will also find some event driven applications using Power Automate or Logic Apps for simple event processing. The processed events are then sent to streaming datasets in Power BI and to some persistent storage layer.

2. Insights into Fabric Real-Time Intelligence

Fabric Real-Time intelligence is a powerful service that empowers everyone in your organization to extract insights and visualize their data in motion.

2.1 Ingestion & Processing

Eventstream in the Microsoft Fabric Real-Time Intelligence lets you bring real-time events into Fabric, transform them, and then route them to various destinations without writing any code. Event streams provide you with multiple source connectors to fetch event data from the various sources.

If you want to connect your own application with an eventstream, you can add a custom endpoint or a custom app as a source. This is described in detail in the following section.

In Eventstream, the event processor editor is a no-code experience that allows you to drag and drop to design the event data processing logic. Here is a link to learn more about event processor editor Event processor editor. You can then route the transformed data to various destinations.

For real-time reporting experience we would recommend using Fabric Eventhouse KQL database. Eventhouses provide a solution for handling and analyzing large volumes of data, particularly in scenarios requiring real-time analytics and exploration.

Figure 1.2

2.2 Deliver & Visualize

You can build real time reports using auto page refresh feature of Power BI with sources that support direct query. Power BI direct query is supported by Fabric Eventhouse KQL database. Use this link to learn more about automatic page refresh feature in PowerBI.

Direct query feature can be used with either of the selected Eventstream destination, Eventhouse or Lakehouse.  However, Eventhouse KQL database offers robust solution for managing and analyzing substantial volumes of real-time data.

Eventhouse is designed to scale efficiently, ensuring effective performance and resource use. This design is beneficial in situations where timely insights are important. Specifically built for time-based, streaming events with features like auto indexing and partitioning based on ingestion time.

3. Azure Stream Analytics

Azure Stream Analytics PowerBI output sends the transformed data to PowerBI to build rich visualizations of analysis results. Migrating Azure Stream Analytics (ASA) jobs that utilize the Power BI output connector involves few key considerations to ensure a seamless transition and maintain real-time data visualization capabilities.

3.1 Ingestion & Processing

For users leveraging Stream Analytics with Power BI output connector in their architecture, who are unable to migrate their solution to Fabric, alternative patterns can be explored following Power BI output connector retirement.

With the processing and data analytics logic implemented in Azure Stream Analytics, you can:

Route Stream Analytics output to Fabric.
Switch to other Stream Analytics connectors that support direct query mode in Power BI.

To push Stream Analytics output data to Fabric you can use Eventstream custom endpoint connector. Here is a link to learn how to add a custom endpoint or a custom app as a source to Eventstream. Add a custom endpoint or custom app source to an eventstream

Once Eventstream custom endpoint setup is complete you should have an event hub namespace and connection details available in the Eventstream. Now back in Azure Stream Analytics you can select Event Hub output connector and add the custom With this setup your existing steam analytics job can now publish data to Fabric Eventstream. Next you can add Eventhouse as your destination in Eventstream and follow the same pattern described above.

Figure 1.3

The Power BI direct query feature is compatible with various ASA and Fabric Eventstream output destinations. Here is a list of other ASA and Eventstream output connectors you can use to build reports in PowerBI with direct query.

Other Azure Stream Analytics output connectors:

SQL Server
PostgreSQL
SynapseSQL
Azure Data Explorer

Other Eventstream destination:

Lakehouse

3.2 Deliver & Visualize

In Power BI you will select Get data with KQL database or one of the sources mentioned above to design your visualization and reports.  

You can choose other sources with import mode if you do not plan to use PowerBI’s auto page refresh feature.

4. Fabric, Real-Time dashboard

For the use cases where you are doing schedule refresh and looking for very short interval auto-refresh experience you can also explore Fabric Real-Time Dashboard. You can natively export Kusto Query Language (KQL) queries to a dashboard as visuals and later modify their underlying queries and visual formatting as needed. In addition to ease of data exploration, this fully integrated dashboard experience provides improved query and visualization performance.

Figure 1.4

5. Call to action

If you have questions on migration recommendations, please reach out to RTISupport.
For any questions on Azure stream analytics please contact askasa@microsoft.com              

Other Useful links

Real-time streaming in Power BI

Automatic page refresh in Power BI Desktop – Power BI | Microsoft Learn

What is Real-Time Intelligence?

Eventhouse overview

Microsoft Fabric event streams – overview

Introduction to Microsoft Fabric Real-Time hub – Microsoft Fabric | Microsoft Learn

Introduction to Azure Stream Analytics – Azure Stream Analytics | Microsoft Learn

​Microsoft Tech Community – Latest Blogs –Read More