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Recap: June 2024 Ambassador AI Projects Demo Day, Showcasing the Power of AI
Building with AI for Global Impact
On June 13, 2024, The Ambassador Projects Demo Day was held virtually on Teams. The event brought together rising developers from all over the world to collaborate and create innovative solutions to real-world problems.
We would like to extend our sincerest thanks to the Gold leads Aayushi Singh, Konstantinos Sitistas, Megha Vishwakarma, Hadil BenAmor, Marjan Hussein for their hard work and dedication in putting together such an amazing event and leading this past cycle of the projects program. Without their tireless efforts, this event would not have been possible. In addition, we would like to thank our Gold milestone judges, Byansi Anthony, Deepthi Balasubramanian, Meerali Naseet, Mudasir Murtaza and Subhashis Paul.
The winning team was Kairos, led by Ambassador Sarosij Ghosh Ray. Their project, Street Savvy, was chosen as the winner because of its innovative approach to solving a real-world problem. Street Savvy is an interactive simulation powered by Azure AI Services that teaches the player about the Indian traffic rules in a gamified manner. This project addresses the critical issue of road safety and driver education in India. Despite existing regulations, India continues to have one of the highest rates of traffic accidents and fatalities in the world. A major contributing factor is the lack of comprehensive understanding and adherence to traffic rules among drivers. Street Savvy tries to teach and raise awareness about Indian traffic rules and road safety in a fun and interactive way so that the youth, drivers or even kids can learn. The judges were impressed with the team’s ability to work together and create a solution that was both practical and innovative.
Other teams that participated in the Ambassadors Projects Program and made it to the final 5 round included Rhino Team, Team Matrix, Catalyst and AgriVision . Each team worked tirelessly to create innovative solutions to real-world problems. Although they did not win, their projects were impressive and showed great promise.
Rhino Team – Team Rhino is crafting an innovative Power Apps solution tailored for the more than 5000 Microsoft Learn Student Ambassadors around the world. This app not only facilitates seamless team formation but also empowers team leads to pinpoint individuals with specific skillsets and team seeker to find adequate team thanks to AI suggestions.
Team Matrix – Aims to develop a Tomato Disease Classifier App using Azure Custom Vision, Azure Web App, and Azure OpenAI Service technology. The app targets farmers and agricultural professionals, providing them with a tool for early detection and classification of common tomato diseases. Leveraging machine learning, computer vision techniques, and natural language processing, the app will assist in identifying diseases promptly, thereby enabling effective disease management and enhancing crop yield and quality.
Catalys – The Code Analysis and Vulnerability Prediction Tool automates code analysis, predicts security issues using machine learning, and offers insights and mitigation strategies to developers.
AgriVision – The 2018 Kerala floods in India were a significant natural disaster caused by unprecedented monsoon rainfall, severely impacting the agricultural sector. This project aims to assess the impact of these floods on agricultural productivity using Normalized Difference Vegetation Index (NDVI) data. By analyzing NDVI data, we aim to provide insights into the extent of agricultural damage and recovery post-flood. The results will help local communities and policymakers improve disaster preparedness, response strategies, and agricultural planning.
Overall, this cycle of Ambassador Projects was a huge success. The event brought together some of the brightest minds in the industry and showcased some truly innovative solutions to real-world problems. We look forward to seeing what the future holds for these talented developers.
Learn more about the Microsoft Learn Student Ambassadors program here.
Microsoft Tech Community – Latest Blogs –Read More
Trying to add month and year labels underneath week numbers along x-axis in Excel graph
I have data throughout many years and am plotting them by week
But I would like to add a month and year label underneath the week number like this example from https://community.fabric.microsoft.com/t5/Desktop/Graph-showing-x-weeks-rolling-data-over-multiple-years/td-p/2295281:
Unlike the example, I would prefer it if the week number didn’t start from 1 again in a new year.
Is there any way to accomplish this without Power BI? Or with Power BI Free Trial version on browser? I would greatly appreciate some help.
I have data throughout many years and am plotting them by week But I would like to add a month and year label underneath the week number like this example from https://community.fabric.microsoft.com/t5/Desktop/Graph-showing-x-weeks-rolling-data-over-multiple-years/td-p/2295281:Unlike the example, I would prefer it if the week number didn’t start from 1 again in a new year. Is there any way to accomplish this without Power BI? Or with Power BI Free Trial version on browser? I would greatly appreciate some help. Read More
surfce 4 pro
after i replaced my windows from 10 to 11 when i plug out the power source my computer turn off
after i replaced my windows from 10 to 11 when i plug out the power source my computer turn off Read More
Conditional formatting based on time elapsed from date
Hi there!
I’m trying to figure out how to make a specific cell in a column appear a certain color based on the amount of time that has elapsed from a date in the cell in the adjacent column… If that makes sense…
So, if cell G4 has a date in it for the last contact with an individual, I want cell H4 to appear green if that date is within the last 30 days, yellow if it’s between 31-60 days ago, orange for 61-90 days ago, red for 91-120 days ago, and black for anything over 120 days ago. I then want to make sure this is a blanket format so that the same rule applies to any cell in the G/H columns (so same rule will apply for G5/H5, G6/H6, and so on).
I saw another post referencing a date being more or less than today’s date in months:
=EDATE(G2,3) <=TODAY()
but I wasn’t sure if there was a way to do it in days past vs months? If not, I can make months work. I just don’t know how to make it a blanket format (vs inputting the formula in each individual cell) w/ multiple color options?
Thanks in advance!
Hi there! I’m trying to figure out how to make a specific cell in a column appear a certain color based on the amount of time that has elapsed from a date in the cell in the adjacent column… If that makes sense… So, if cell G4 has a date in it for the last contact with an individual, I want cell H4 to appear green if that date is within the last 30 days, yellow if it’s between 31-60 days ago, orange for 61-90 days ago, red for 91-120 days ago, and black for anything over 120 days ago. I then want to make sure this is a blanket format so that the same rule applies to any cell in the G/H columns (so same rule will apply for G5/H5, G6/H6, and so on).I saw another post referencing a date being more or less than today’s date in months:=EDATE(G2,3) <=TODAY()but I wasn’t sure if there was a way to do it in days past vs months? If not, I can make months work. I just don’t know how to make it a blanket format (vs inputting the formula in each individual cell) w/ multiple color options? Thanks in advance! Read More
Mouse Driver
I have and Alienware R17 laptop running Win 10. I want to restore Win 10. The laptop does all the way to the final stages and then quits due to some unspecified error. When Win 10 restarts I get a message saying that some error occurred and that nothing was changed. Then the Win 10 reverses whatever changes it tried to make. Also, the mouse doesn’t work because Win 10 keeps loading a problematic driver. I’ve tried to update the driver, uninstall the driver but I get the message that the latest driver is already installed. I’m wondering if I can solve the mouse driver error, then maybe I can restore Win 10 to its pristine state. I’ve already ran anti-virus scans. I’m at my wit end. Please tell me that there is a solution. Thanks, in advance.
I have and Alienware R17 laptop running Win 10. I want to restore Win 10. The laptop does all the way to the final stages and then quits due to some unspecified error. When Win 10 restarts I get a message saying that some error occurred and that nothing was changed. Then the Win 10 reverses whatever changes it tried to make. Also, the mouse doesn’t work because Win 10 keeps loading a problematic driver. I’ve tried to update the driver, uninstall the driver but I get the message that the latest driver is already installed. I’m wondering if I can solve the mouse driver error, then maybe I can restore Win 10 to its pristine state. I’ve already ran anti-virus scans. I’m at my wit end. Please tell me that there is a solution. Thanks, in advance. Read More
Teams for Education: Generative AI Instruction Limit
In Teams for Education, generative AI instructions are capped at 10 generations. Do the number of available generations refresh after a period of time, or do users only receive 10 generations for their lifetime?
In Teams for Education, generative AI instructions are capped at 10 generations. Do the number of available generations refresh after a period of time, or do users only receive 10 generations for their lifetime? Read More
New Windows corporate device identifier feature with Microsoft Intune: Everything you need to know
By: Madison Holdaas, Sr Product Manager | Microsoft Intune
How identifying corporate devices has worked in Intune
As an administrator, you want to make sure that only authorized and compliant devices can access your organization’s resources and data. To do that, you need to identify which devices are corporate-owned and which are personal. However, this isn’t always easy, especially when you have a large and diverse fleet of devices running different operating systems and platforms.
Today, Intune has a variety of methods to identify a device as “corporate” for Windows platform. If a device hasn’t enrolled using one of our true corporate methods, we do our best to determine an unknown device’s ownership by how the user enrolled the device. For instance, if a user automatically enrolls by registering the device to Microsoft Entra through Windows settings, then we determine that device to be corporate. If a user automatically enrolls by adding a work account from Windows settings instead, then the device is marked personal by Intune.
How enrollment restrictions have worked when blocking personal devices
One way to prevent personal or unknown devices from enrolling in your tenant is to use enrollment restrictions. Enrollment restrictions are policies that you can create and assign to groups of users or devices to control who can enroll which devices and how many. You can create two types of enrollment restrictions: device type restrictions and device limit restrictions.
Device type enrollment restrictions allow you to block or allow specific types of devices from enrolling, such as Windows, iOS, Android, or macOS. You can also block or allow for specific configurations, such as blocking personally owned or unknown devices. The setting to block personally owned devices prevents the following from being enrolled, even though they are assumed corporate by Intune when allowed to enroll:
Automatic MDM enrollment with Microsoft Entra join during Windows setup
Automatic MDM enrollment with Microsoft Entra join from Windows Settings
Automatic MDM enrollment with Microsoft Entra join or hybrid Entra join via Windows Autopilot for existing devices
New corporate device identifiers for Windows
The new Windows corporate identifier feature is a solution that can help you identify and manage your corporate Windows devices more easily and securely. The feature allows you to upload a CSV file with the serial number, manufacturer, and model of your known Windows devices to your tenant. This marks the devices as corporate in the Microsoft Intune admin center and applies the appropriate policies and settings to them once they enroll into your tenant. Note that the feature only works for Windows 11, version 22H2 and later with KB5035942 (OS Builds 22621.3374 and 22631.3374) or newer.
Important: Enrollment device type restrictions are only editable by the Intune Service Administrator or Global Administrator. Corporate device identifiers have their own permission that must be assigned. Since these permissions are not the same, confirm that any existing enrollment restrictions will not be impacted before uploading a corporate device identifier.
To use the new feature, follow these steps:
Create a CSV file with the serial number, manufacturer, and model of your corporate Windows devices. You can use any tool or method to generate the CSV file, as long as it follows the format and requirements specified in the documentation.
In the Intune admin center, upload the CSV file to your tenant. You can find the upload option under Devices > Windows > Corporate identifiers. You can upload up to 5,000 devices or 5MB in a CSV. If you need to upload more, we recommend using PowerShell and interacting with the Microsoft Graph API directly.
Verify that the upload was successful and that the devices are marked as corporate in the Intune admin center. You can view the status and details of the upload under Devices > Windows > Corporate identifiers. You can also view the device ownership and other properties of the devices under Devices > All devices.
Some enrollment methods will always be considered corporate enrollment because we trust devices enrolling through these methods are known devices. Once an admin has uploaded a single Windows corporate identifier, the way we define Corporate and Personal changes to the following in the table:
Windows enrollment types
Without corporate identifiers
With corporate identifiers
The device enrolls through Windows Autopilot.
Corporate
Corporate
The device enrolls through GPO, or automatic enrollment from Configuration Manager for co-management.
Corporate
Corporate
The device enrolls through a bulk provisioning package.
Corporate
Corporate
The enrolling user is using a device enrollment manager account.
Corporate
Corporate
The device enrolls through Azure Virtual desktop (non-hybrid)
Corporate
Corporate
Automatic MDM enrollment with Microsoft Entra join during Windows setup.
Corporate, but blocked by Personal enrollment restriction
Personal
Automatic MDM enrollment with Microsoft Entra join from Windows Settings.
Corporate, but blocked by Personal enrollment restriction
Personal
Automatic MDM enrollment with Microsoft Entra join or hybrid Entra join via Windows Autopilot for existing devices.
Corporate, but blocked by Personal enrollment restriction
Personal
Automatic MDM enrollment with Add Work Account from Windows Settings.
Personal
Personal
MDM enrollment only option from Windows Settings.
Personal
Personal
Enrollment using the Intune Company Portal app.
Personal
Personal
Enrollment via a Microsoft 365 app, which occurs when users select the Allow my organization to manage my device option during app sign-in.
Personal
Personal
Admins that want to use the existing enrollment method logic to determine corporate versus personal (i.e. the “Without corporate identifiers” column) can just delete or remove all Windows corporate identifiers and ownership goes back to behaving as previously done in Intune.
New enrollment restriction experience using model and manufacturer device properties in filters
The new Windows corporate identifier feature also enables a new enrollment restriction experience that allows you to use the model and manufacturer device properties in filters to block devices from enrolling more granularly. You can block specific models or manufacturers of Windows devices from enrolling, such as Manufacturer = Microsoft or Model = VM. Note that model and manufacturer properties only work for Windows 11 version 22H2 and above at enrollment time.
To use the new enrollment restriction experience, navigate to the Intune admin center and follow these steps:
Create a device filter with the model and manufacturer device properties. You can find the device filter option under Devices > Filters. You can create up to 100 device filters per tenant, and each device filter can have up to 10 conditions.
Create an enrollment restriction policy with the device filter. You can find the enrollment restriction option under Devices > Enrollment> Device platform restrictions. You can assign the device filter to your enrollment restriction policy in the Assignments tab.
Assign the enrollment restriction policy to a group of users. You can assign the policy to any group that you have created or synced in your tenant, such as security groups or dynamic groups. You can also assign the policy to the default group, which applies to all users in your tenant. Reminder that enrollment restrictions are user based – so they don’t apply to user-less enrollments.
Note that since model and manufacturer properties only work for Windows 11 version 22H2 and above – to address unsupported versions – we recommend including the null values of manufacturer and model.
Note – Windows 10 will be a supported feature starting July 9th – devices will need to be updated to the following KB: KB5039299.
With this new feature, you can easily distinguish between corporate and personal devices and apply different enrollment policies accordingly. Additionally, you can leverage the model and manufacturer device properties to create more granular filters to block unwanted devices from enrolling.
If you have any questions or feedback, leave a comment below or reach out to us on X @IntuneSuppTeam.
Microsoft Tech Community – Latest Blogs –Read More
Why are disabled users removed from Org Chart?
Someone brought to my attention that when a user leaves the organisation and the account deleted, the user is removed from the org chart with every other users who directly or indirectly report to that user. I thought why not just disable the user first and give some time to allow updating the manager field for those who directly report to that user. So I did a to disable a user account but was surprised to see that even if the account was only disabled, it still got removed from the org chart.
What’s the reason behind this? An account can be disabled temporarily for any reason and does not necessary mean the user is no longer with the organisation. Removing the disabled user and everyone reports to them from the org chart does not make much sense to me.
Someone brought to my attention that when a user leaves the organisation and the account deleted, the user is removed from the org chart with every other users who directly or indirectly report to that user. I thought why not just disable the user first and give some time to allow updating the manager field for those who directly report to that user. So I did a to disable a user account but was surprised to see that even if the account was only disabled, it still got removed from the org chart. What’s the reason behind this? An account can be disabled temporarily for any reason and does not necessary mean the user is no longer with the organisation. Removing the disabled user and everyone reports to them from the org chart does not make much sense to me. Read More
Smartsheet Conversation functionality in SP Lists
Hi All
I want to build a SP List that has the functionality found in Smartsheet where you are able to have a conversation on a row like it is in smartsheet. SP List has the functionality to post comments on a row, but at this point it’s not possible to upload any attachments. I want to build out the frontend of this List in Power Apps. Is there a way that this could be done to closely resemble the functionality as in smartsheet? Here is a video of this functionality: https://www.youtube.com/watch?v=8K-ZK6b7u8E
Hi All I want to build a SP List that has the functionality found in Smartsheet where you are able to have a conversation on a row like it is in smartsheet. SP List has the functionality to post comments on a row, but at this point it’s not possible to upload any attachments. I want to build out the frontend of this List in Power Apps. Is there a way that this could be done to closely resemble the functionality as in smartsheet? Here is a video of this functionality: https://www.youtube.com/watch?v=8K-ZK6b7u8E Read More
Fundamentals of machine learning
Machine learning is in many ways the intersection of two disciplines – data science and software engineering. The goal of machine learning is to use data to create a predictive model that can be incorporated into a software application or service. To achieve this goal requires collaboration between data scientists who explore and prepare the data before using it to train a machine learning model, and software developers who integrate the models into applications where they’re used to predict new data values (a process known as inferencing).
What is machine learning?
Machine learning has its origins in statistics and mathematical modeling of data. The fundamental idea of machine learning is to use data from past observations to predict unknown outcomes or values. For example:
The proprietor of an ice cream store might use an app that combines historical sales and weather records to predict how many ice creams they’re likely to sell on a given day, based on the weather forecast.A doctor might use clinical data from past patients to run automated tests that predict whether a new patient is at risk from diabetes based on factors like weight, blood glucose level, and other measurements.A researcher in the Antarctic might use past observations automate the identification of different penguin species (such as Adelie, Gentoo, or Chinstrap) based on measurements of a bird’s flippers, bill, and other physical attributes.
Machine learning as a function
Because machine learning is based on mathematics and statistics, it’s common to think about machine learning models in mathematical terms. Fundamentally, a machine learning model is a software application that encapsulates a function to calculate an output value based on one or more input values. The process of defining that function is known as training. After the function has been defined, you can use it to predict new values in a process called inferencing.
Let’s explore the steps involved in training and inferencing.
The training data consists of past observations. In most cases, the observations include the observed attributes or features of the thing being observed, and the known value of the thing you want to train a model to predict (known as the label).
In mathematical terms, you’ll often see the features referred to using the shorthand variable name x, and the label referred to as y. Usually, an observation consists of multiple feature values, so x is actually a vector (an array with multiple values), like this: [x1,x2,x3,…].
To make this clearer, let’s consider the examples described previously:
In the ice cream sales scenario, our goal is to train a model that can predict the number of ice cream sales based on the weather. The weather measurements for the day (temperature, rainfall, windspeed, and so on) would be the features (x), and the number of ice creams sold on each day would be the label (y).In the medical scenario, the goal is to predict whether or not a patient is at risk of diabetes based on their clinical measurements. The patient’s measurements (weight, blood glucose level, and so on) are the features (x), and the likelihood of diabetes (for example, 1 for at risk, 0 for not at risk) is the label (y).In the Antarctic research scenario, we want to predict the species of a penguin based on its physical attributes. The key measurements of the penguin (length of its flippers, width of its bill, and so on) are the features (x), and the species (for example, 0 for Adelie, 1 for Gentoo, or 2 for Chinstrap) is the label (y).
An algorithm is applied to the data to try to determine a relationship between the features and the label, and generalize that relationship as a calculation that can be performed on x to calculate y. The specific algorithm used depends on the kind of predictive problem you’re trying to solve (more about this later), but the basic principle is to try to fit a function to the data, in which the values of the features can be used to calculate the label.
The result of the algorithm is a model that encapsulates the calculation derived by the algorithm as a function – let’s call it f. In mathematical notation:
y = f(x)
Now that the training phase is complete, the trained model can be used for inferencing. The model is essentially a software program that encapsulates the function produced by the training process. You can input a set of feature values, and receive as an output a prediction of the corresponding label. Because the output from the model is a prediction that was calculated by the function, and not an observed value, you’ll often see the output from the function shown as ŷ (which is rather delightfully verbalized as “y-hat”).
Types of machine learning
There are multiple types of machine learning, and you must apply the appropriate type depending on what you’re trying to predict. A breakdown of common types of machine learning is shown in the following diagram.
Supervised machine learning
Supervised machine learning is a general term for machine learning algorithms in which the training data includes both feature values and known label values. Supervised machine learning is used to train models by determining a relationship between the features and labels in past observations, so that unknown labels can be predicted for features in future cases.
Regression
Regression is a form of supervised machine learning in which the label predicted by the model is a numeric value. For example:
The number of ice creams sold on a given day, based on the temperature, rainfall, and windspeed.The selling price of a property based on its size in square feet, the number of bedrooms it contains, and socio-economic metrics for its location.The fuel efficiency (in miles-per-gallon) of a car based on its engine size, weight, width, height, and length.
Classification
Classification is a form of supervised machine learning in which the label represents a categorization, or class. There are two common classification scenarios.
Binary classification
In binary classification, the label determines whether the observed item is (or isn’t) an instance of a specific class. Or put another way, binary classification models predict one of two mutually exclusive outcomes. For example:
Whether a patient is at risk for diabetes based on clinical metrics like weight, age, blood glucose level, and so on.Whether a bank customer will default on a loan based on income, credit history, age, and other factors.Whether a mailing list customer will respond positively to a marketing offer based on demographic attributes and past purchases.
In all of these examples, the model predicts a binary true/false or positive/negative prediction for a single possible class.
Multiclass classification
Multiclass classification extends binary classification to predict a label that represents one of multiple possible classes. For example,
The species of a penguin (Adelie, Gentoo, or Chinstrap) based on its physical measurements.The genre of a movie (comedy, horror, romance, adventure, or science fiction) based on its cast, director, and budget.
In most scenarios that involve a known set of multiple classes, multiclass classification is used to predict mutually exclusive labels. For example, a penguin can’t be both a Gentoo and an Adelie. However, there are also some algorithms that you can use to train multilabel classification models, in which there may be more than one valid label for a single observation. For example, a movie could potentially be categorized as both science fiction and comedy.
Unsupervised machine learning
Unsupervised machine learning involves training models using data that consists only of feature values without any known labels. Unsupervised machine learning algorithms determine relationships between the features of the observations in the training data.
Clustering
The most common form of unsupervised machine learning is clustering. A clustering algorithm identifies similarities between observations based on their features, and groups them into discrete clusters. For example:
Group similar flowers based on their size, number of leaves, and number of petals.Identify groups of similar customers based on demographic attributes and purchasing behavior.
In some ways, clustering is similar to multiclass classification; in that it categorizes observations into discrete groups. The difference is that when using classification, you already know the classes to which the observations in the training data belong; so the algorithm works by determining the relationship between the features and the known classification label. In clustering, there’s no previously known cluster label and the algorithm groups the data observations based purely on similarity of features.
In some cases, clustering is used to determine the set of classes that exist before training a classification model. For example, you might use clustering to segment your customers into groups, and then analyze those groups to identify and categorize different classes of customer (high value – low volume, frequent small purchaser, and so on). You could then use your categorizations to label the observations in your clustering results and use the labeled data to train a classification model that predicts to which customer category a new customer might belong.
Machine learning is in many ways the intersection of two disciplines – data science and software engineering. The goal of machine learning is to use data to create a predictive model that can be incorporated into a software application or service. To achieve this goal requires collaboration between data scientists who explore and prepare the data before using it to train a machine learning model, and software developers who integrate the models into applications where they’re used to predict new data values (a process known as inferencing). What is machine learning? Machine learning has its origins in statistics and mathematical modeling of data. The fundamental idea of machine learning is to use data from past observations to predict unknown outcomes or values. For example:The proprietor of an ice cream store might use an app that combines historical sales and weather records to predict how many ice creams they’re likely to sell on a given day, based on the weather forecast.A doctor might use clinical data from past patients to run automated tests that predict whether a new patient is at risk from diabetes based on factors like weight, blood glucose level, and other measurements.A researcher in the Antarctic might use past observations automate the identification of different penguin species (such as Adelie, Gentoo, or Chinstrap) based on measurements of a bird’s flippers, bill, and other physical attributes. Machine learning as a functionBecause machine learning is based on mathematics and statistics, it’s common to think about machine learning models in mathematical terms. Fundamentally, a machine learning model is a software application that encapsulates a function to calculate an output value based on one or more input values. The process of defining that function is known as training. After the function has been defined, you can use it to predict new values in a process called inferencing.Let’s explore the steps involved in training and inferencing. The training data consists of past observations. In most cases, the observations include the observed attributes or features of the thing being observed, and the known value of the thing you want to train a model to predict (known as the label).In mathematical terms, you’ll often see the features referred to using the shorthand variable name x, and the label referred to as y. Usually, an observation consists of multiple feature values, so x is actually a vector (an array with multiple values), like this: [x1,x2,x3,…].To make this clearer, let’s consider the examples described previously:In the ice cream sales scenario, our goal is to train a model that can predict the number of ice cream sales based on the weather. The weather measurements for the day (temperature, rainfall, windspeed, and so on) would be the features (x), and the number of ice creams sold on each day would be the label (y).In the medical scenario, the goal is to predict whether or not a patient is at risk of diabetes based on their clinical measurements. The patient’s measurements (weight, blood glucose level, and so on) are the features (x), and the likelihood of diabetes (for example, 1 for at risk, 0 for not at risk) is the label (y).In the Antarctic research scenario, we want to predict the species of a penguin based on its physical attributes. The key measurements of the penguin (length of its flippers, width of its bill, and so on) are the features (x), and the species (for example, 0 for Adelie, 1 for Gentoo, or 2 for Chinstrap) is the label (y).An algorithm is applied to the data to try to determine a relationship between the features and the label, and generalize that relationship as a calculation that can be performed on x to calculate y. The specific algorithm used depends on the kind of predictive problem you’re trying to solve (more about this later), but the basic principle is to try to fit a function to the data, in which the values of the features can be used to calculate the label.The result of the algorithm is a model that encapsulates the calculation derived by the algorithm as a function – let’s call it f. In mathematical notation:y = f(x)Now that the training phase is complete, the trained model can be used for inferencing. The model is essentially a software program that encapsulates the function produced by the training process. You can input a set of feature values, and receive as an output a prediction of the corresponding label. Because the output from the model is a prediction that was calculated by the function, and not an observed value, you’ll often see the output from the function shown as ŷ (which is rather delightfully verbalized as “y-hat”). Types of machine learningThere are multiple types of machine learning, and you must apply the appropriate type depending on what you’re trying to predict. A breakdown of common types of machine learning is shown in the following diagram. Supervised machine learningSupervised machine learning is a general term for machine learning algorithms in which the training data includes both feature values and known label values. Supervised machine learning is used to train models by determining a relationship between the features and labels in past observations, so that unknown labels can be predicted for features in future cases.RegressionRegression is a form of supervised machine learning in which the label predicted by the model is a numeric value. For example:The number of ice creams sold on a given day, based on the temperature, rainfall, and windspeed.The selling price of a property based on its size in square feet, the number of bedrooms it contains, and socio-economic metrics for its location.The fuel efficiency (in miles-per-gallon) of a car based on its engine size, weight, width, height, and length.ClassificationClassification is a form of supervised machine learning in which the label represents a categorization, or class. There are two common classification scenarios. Binary classificationIn binary classification, the label determines whether the observed item is (or isn’t) an instance of a specific class. Or put another way, binary classification models predict one of two mutually exclusive outcomes. For example:Whether a patient is at risk for diabetes based on clinical metrics like weight, age, blood glucose level, and so on.Whether a bank customer will default on a loan based on income, credit history, age, and other factors.Whether a mailing list customer will respond positively to a marketing offer based on demographic attributes and past purchases.In all of these examples, the model predicts a binary true/false or positive/negative prediction for a single possible class.Multiclass classificationMulticlass classification extends binary classification to predict a label that represents one of multiple possible classes. For example,The species of a penguin (Adelie, Gentoo, or Chinstrap) based on its physical measurements.The genre of a movie (comedy, horror, romance, adventure, or science fiction) based on its cast, director, and budget.In most scenarios that involve a known set of multiple classes, multiclass classification is used to predict mutually exclusive labels. For example, a penguin can’t be both a Gentoo and an Adelie. However, there are also some algorithms that you can use to train multilabel classification models, in which there may be more than one valid label for a single observation. For example, a movie could potentially be categorized as both science fiction and comedy. Unsupervised machine learningUnsupervised machine learning involves training models using data that consists only of feature values without any known labels. Unsupervised machine learning algorithms determine relationships between the features of the observations in the training data.ClusteringThe most common form of unsupervised machine learning is clustering. A clustering algorithm identifies similarities between observations based on their features, and groups them into discrete clusters. For example:Group similar flowers based on their size, number of leaves, and number of petals.Identify groups of similar customers based on demographic attributes and purchasing behavior.In some ways, clustering is similar to multiclass classification; in that it categorizes observations into discrete groups. The difference is that when using classification, you already know the classes to which the observations in the training data belong; so the algorithm works by determining the relationship between the features and the known classification label. In clustering, there’s no previously known cluster label and the algorithm groups the data observations based purely on similarity of features.In some cases, clustering is used to determine the set of classes that exist before training a classification model. For example, you might use clustering to segment your customers into groups, and then analyze those groups to identify and categorize different classes of customer (high value – low volume, frequent small purchaser, and so on). You could then use your categorizations to label the observations in your clustering results and use the labeled data to train a classification model that predicts to which customer category a new customer might belong. Read More
Using Azure Function to download files from an external site and storage them in the VM
Hi All,
I created a function to download files from an external site and it’s working fine from my local computer. Is it possible to modify the code so it will download the files to a specific folder on the VM after deploying the function? If so, how? Any suggestions is much appreciated.
TIA
Steve
Hi All, I created a function to download files from an external site and it’s working fine from my local computer. Is it possible to modify the code so it will download the files to a specific folder on the VM after deploying the function? If so, how? Any suggestions is much appreciated. TIASteve Read More
Modernized Excel Grid
Our latest update for web users brings you a host of powerful features designed to make your spreadsheet tasks simpler, faster, and more enjoyable. From effortless resizing and streamlined inserts to enhanced navigation and easy cell highlighting, discover how these modern tools can revolutionize your workflows.
Resize rows and columns with ease
Quickly resize rows and columns for better data visibility and presentation. Hover over the border of a row or column header, click and hold the handles, then drag to resize.
Simplified insert options
Our new simplified interface makes adding rows, or columns to your spreadsheet a snap. Just hover over the respective row or column header and then click on the small circles (convert to + on hover). Streamline your workflow and get more done in less time.
Streamlined unhide feature
Show hidden rows or columns with one click and get a complete view of your data instantly. Just hover over the row or column header and then select the small arrows that appear.
Freeze panes for better navigation
Keep important headers or columns visible as you scroll to ensure that important information stays visible, no matter how far you scroll down or across your spreadsheet.
To do so, drag the handles in the top left corner of the headers and drag them to the desired position. To change existing freeze panes, just drag the freeze pane line.
Drag & drop to rearrange elements
Effortlessly rearrange elements in your worksheet with drag and drop, making data organization a breeze.
To try the drag and drop feature, select any row or column, hold and drag when the cursor shows the hand icon, and then drop in any other row or column.
Highlight cells for clarity
Highlight important cells to emphasize critical information and improve readability. To use this feature, just select a row, column, range of cells, or individual cell.
Availability
These features are currently rolling out to all Excel for the web users.
Microsoft Tech Community – Latest Blogs –Read More
Celebrating 2024 Partner of the Year Awards Social Impact Category Winners
Congratulations to the winners and finalists for the 2024 Microsoft Partner of the Year Awards!
The Social Impact category highlights partners accelerating innovation and technology applications that are making a difference for individuals, customers, and communities around the world. We read so many strong entries of partners embracing the possibilities that AI is creating and taking the lead in applying this latest technology to solve societal challenges, creating more inclusive, equitable, and sustainable models of growth.
Together with the team behind the Partner of the Year Awards Social Impact category, we’re thrilled to honor winning partners for the inclusion changemaker, sustainability changemaker and community response awards.
POTYA Community Response winner: Simpson Associates | United Kingdom
The Community Response Partner of the Year Award recognizes a partner organization that is providing innovative and unique services or solutions based on Microsoft technologies, helping solve challenges faced by communities and making a significant social impact during unprecedented times.
Simpson Associates has been a key technical delivery partner for the Tackling Organized Exploitation (TOEX) Program in two ways. First, it implemented an Azure-based TOEX Data Platform, the first data solution to combine force, regional and national data sets from UK law enforcement agencies to create an enhanced intelligence picture of organized exploitation. Second, it delivered enhanced technical capabilities through predictive analytics to strengthen the efficiency and effectiveness of policing.
POTYA Inclusion Changemaker winner: Legal Interact | South Africa
The Inclusion Changemaker Partner of the Year Award recognizes a partner organization that excels at providing innovative and unique services or solutions based on Microsoft technologies that help customers solve challenges of diverse representation, economic access, digital inclusion, and/or accessibility.
Legal Interact’s “My AI Lawyer” bridges justice gaps in South Africa by leveraging Azure Cloud and AI on WhatsApp, offering scalable legal advice and affordable access to law. This innovation marks a significant step towards a more just society by enabling inclusive access to critical legal services.
POTYA Sustainability Changemaker winner: EY | United States
The Sustainability Changemaker Partner of the Year Award recognizes a partner organization that excels at providing innovative and unique services or solutions based on Microsoft technologies that help customers solve challenges of sustainable digital transformation.
Together, EY and Microsoft help customers make better decisions, meet regulatory requirements and generate value at every stage of their environmental, social and governance-based investing (ESG) and sustainability journey. By combining EY’s deep and broad technical expertise with Microsoft’s reputation for cutting-edge technological solutions, EY has helped over 100 clients accelerate their decarbonization journey, creating sustainable value and helping them deliver on their ESG goals.
Congratulations also to our Partner of the Year Award Social Impact category finalists!
POTYA Community Response
POTYA Inclusion Changemaker
POTYA Sustainability Changemaker
BDO Digital
Fellowmind
Kin + Carta
Join us at MCAPS Start for Partners and at Microsoft Ignite (November 18-22, 2024) to celebrate the outstanding achievements of these partners. Learn and find winners and finalists across all categories at the Partner of the Year Awards website.
Microsoft Tech Community – Latest Blogs –Read More
Flip(grid) in Teams
What gives – we were testing Flip(grid) as a Teams app, but it has now disappeared and is no longer available for installation. A number of our faculty would like to continue to use Flip. Will it be available in the future?
-Michael
What gives – we were testing Flip(grid) as a Teams app, but it has now disappeared and is no longer available for installation. A number of our faculty would like to continue to use Flip. Will it be available in the future? -Michael Read More
Azure sponsorship not showing up
After following the instructions here about the Azure grant:
https://nonprofit.microsoft.com/en-us/offers/azure
I received an email saying it was approved and the subscription shows up in my Azure portal but I’m not seeing the sponsorship/balance in the Azure sponsorships portal:
https://www.microsoftazuresponsorships.com/Balance#
Am I missing something, or does it just take a while until it shows up there?
After following the instructions here about the Azure grant:https://nonprofit.microsoft.com/en-us/offers/azure I received an email saying it was approved and the subscription shows up in my Azure portal but I’m not seeing the sponsorship/balance in the Azure sponsorships portal:https://www.microsoftazuresponsorships.com/Balance# Am I missing something, or does it just take a while until it shows up there? Read More
Azure Event Hub Data Connector Manage Error
When trying to manage the Azure Event Hub Data Connector I am getting this error in the attached screenshot. I can’t go any further.
When trying to manage the Azure Event Hub Data Connector I am getting this error in the attached screenshot. I can’t go any further. Read More
UNIQUE Function not working
Dear Experts,
I have a scenario , as below:-
Column A and Column B has values as below:-
In Cells, A14,B14 , I want the Count of the UNIQUE values of those columns, so A14== 2, B14==3,
I tried to use comb of COUNTIF and UNIQUE, but seems something is broken, or my logic seems not correct.
Secondly, in A15,B15 want to print the unique values( so A15== 0,1 ; B15 == 0,1,2)
I can use remove duplicated from data tab, but I don’t want that as I need to use these cells output(A14/B14) further, without modifying the columns A and B, which this option of remove-duplicate does..
Thanks in Advance,
Br,
Anupam
Dear Experts, I have a scenario , as below:-Column A and Column B has values as below:-In Cells, A14,B14 , I want the Count of the UNIQUE values of those columns, so A14== 2, B14==3,I tried to use comb of COUNTIF and UNIQUE, but seems something is broken, or my logic seems not correct.Secondly, in A15,B15 want to print the unique values( so A15== 0,1 ; B15 == 0,1,2)I can use remove duplicated from data tab, but I don’t want that as I need to use these cells output(A14/B14) further, without modifying the columns A and B, which this option of remove-duplicate does.. Thanks in Advance,Br,Anupam Read More
Did Microsoft start blocking non TLS emails?
I’m starting to get complaints about external emails being rejected with the error: Remote host said: 454 4.7.0 Connection is not TLS encrypted. Recipient organization requires TLS.
This appears to have started on 6/27/2024. I haven’t made any recent changes, so it seems like a change on Microsoft’s end?
I’m starting to get complaints about external emails being rejected with the error: Remote host said: 454 4.7.0 Connection is not TLS encrypted. Recipient organization requires TLS. This appears to have started on 6/27/2024. I haven’t made any recent changes, so it seems like a change on Microsoft’s end? Read More
Contained Database – Migrating to a contained database – dm_db_uncontained_entities
Hi,
i’m currently experimenting with contained databases.
I have started migrating to partially contained database following this guide: migrate-to-a-partially-contained-database.
The query
SELECT SO.name, UE.* FROM sys.dm_db_uncontained_entities AS UE
LEFT JOIN sys.objects AS SO
ON UE.major_id = SO.object_id;
still shows me a few usages of features like “Deferred Name Resolution”, “Database Principal” that could potentially break the contained database boundary.
“Deferred Name Resolution” is due to temp db usage.
Is there any need to worry about that? TempDB is a modified but still usable feature.
Other then that i got sa login mapped to the dbo user.
Now i could have another login mapped to the dbo user i guess, eventually using “sp_changedbowner”, but i guess that would not change the fact that its an uncontained entity.
The query also shows me another sql user that is not mapped to a login, value of SO.name is syspriorities.
Any guidance or comment on this is highly appreciated.
Thank you,
Richard
Hi, i’m currently experimenting with contained databases.I have started migrating to partially contained database following this guide: migrate-to-a-partially-contained-database. The query SELECT SO.name, UE.* FROM sys.dm_db_uncontained_entities AS UE
LEFT JOIN sys.objects AS SO
ON UE.major_id = SO.object_id; still shows me a few usages of features like “Deferred Name Resolution”, “Database Principal” that could potentially break the contained database boundary. “Deferred Name Resolution” is due to temp db usage.Is there any need to worry about that? TempDB is a modified but still usable feature. Other then that i got sa login mapped to the dbo user.Now i could have another login mapped to the dbo user i guess, eventually using “sp_changedbowner”, but i guess that would not change the fact that its an uncontained entity. The query also shows me another sql user that is not mapped to a login, value of SO.name is syspriorities. Any guidance or comment on this is highly appreciated. Thank you, Richard Read More
Defender for Cloud tags disks as not encrypted when FSLogix profiles disk are attached.
We’ve come across an issue where VM disks that are encrypted are flagged as not encrypted whenever a FSLogix profile disk is connected.
is there a possibility of modifying the detection algorithm so fslogix profile disks arent flagging these machines as not encrypted.
After lengthy support ticket with Microsoft it seems there is no realistic method to encrypt/unencrypt fslogix profile disks also.
We’ve come across an issue where VM disks that are encrypted are flagged as not encrypted whenever a FSLogix profile disk is connected.is there a possibility of modifying the detection algorithm so fslogix profile disks arent flagging these machines as not encrypted. After lengthy support ticket with Microsoft it seems there is no realistic method to encrypt/unencrypt fslogix profile disks also. Read More