Zara  Bryant

Zara Bryant


Azure Communication Services with Microsoft Teams Interoperability

Join us to learn more about the latest on Azure Communication Services & Microsoft Teams interoperability and how you can use these services to build custom meeting experiences that interact with Microsoft Teams.

Microsoft Build 2021

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Azure Communication Services with Microsoft Teams Interoperability
Chet  Lubowitz

Chet Lubowitz


How to Install Microsoft Teams on Ubuntu 20.04

Microsoft Teams is a communication platform used for Chat, Calling, Meetings, and Collaboration. Generally, it is used by companies and individuals working on projects. However, Microsoft Teams is available for macOS, Windows, and Linux operating systems available now.

In this tutorial, we will show you how to install Microsoft Teams on Ubuntu 20.04 machine. By default, Microsoft Teams package is not available in the Ubuntu default repository. However we will show you 2 methods to install Teams by downloading the Debian package from their official website, or by adding the Microsoft repository.

Install Microsoft Teams on Ubuntu 20.04

1./ Install Microsoft Teams using Debian installer file

01- First, navigate to teams app downloads page and grab the Debian binary installer. You can simply obtain the URL and pull the binary using wget;

$ wget${VERSION}_amd64.deb

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Ignite 2020: Introducing Azure Communication Services

Microsoft Teams’ growth has exploded as the COVID-19 pandemic has forced more and more companies to shift to home working and virtual meetings. With more than 5 billion daily meeting minutes, Teams relies heavily on Azure’s global network of fiber-connected hyperscale data centers and its growing number of metroscale edge sites. It’s a powerful set of technologies, with support for text, voice, and video communications, all wrapped up in apps that run on the Web, on PCs, and on mobile devices.

What if you could take advantage of those same services in your own code, using them to add global, stress-tested, reliable communications features without spending time learning how to construct calls in WebRTC? With the launch of a public preview of Azure Communication Services (ACS), now you can. Microsoft is unbundling many of Teams’ foundational services, turning them into APIs that can be quickly integrated into new and existing apps.

As Scott Van Vliet, CVP, Intelligent Communications at Azure noted, “The service that my team runs that powers Teams has been built on Azure since day one, so we were kind of born in the cloud with Teams. And thinking about the value we get from being on the Azure platform, we started thinking about what are ways in which we think people can leverage this platform?” The pandemic may have accelerated Microsoft’s plans to release ACS to help companies improve their remote working, but it’s an expansion that’s clearly been in the works for some time. The mature APIs used by Teams are ready to launch a fully fledged service that’s able to support as wide a set of scenarios as possible.

Building Teams’ back-end services into your code

Building on the internal APIs used in Teams, ACS is designed to support many different communication scenarios: one-to-one, one-to-many, many-to-many, browser, apps, bots, and even the public switched telephony network. You can also mix different options in the same app, much like Teams where you can change your communications mode as your interactions deepen or become more focused. It’s easy to image an ACS-based customer service application starting as text chat in a bot and then moving to a human agent when more complex answers are required, or even to a video call if problem diagnosis calls for images.

Developing with ACS is much like working with any other Azure service. Microsoft has provided a series of SDKs and client libraries to help you build code, treating ACS as a data plane that links application end points routing calls and messages. Browser-based applications can use the provided ACS JavaScript libraries. Similarly you can build these services into native desktop and mobile apps, tying in other Azure services like Windows Notifications to add additional features, or working with platform-specific APIs such as Google Firebase on Android and Apple Push Notifications on iOS.

#azure communication services #azure #communication services #communication #ignite 2020

Ssekidde  Nat

Ssekidde Nat


Developing Middleware With Microsoft Azure Service Bus And Functions


A middleware is a software service that glues together multiple services. In today’s business needs, multiple software services and technologies need to work together and communicate with each other. It is not necessary that these distributed software services are compatible with each other and will be able to communicate.

Example Business Case

We have to develop a software service in which we have geo-coordinates of a location and we need to get weather information of the city based on those coordinates. We have a system X that needs to communicate with another system Y. These are distributed systems. System X has information about geo coordinates and system Y will store weather information of the city based on those coordinates.


We will develop a middleware between system X and system Y.

Middleware Architecture

  1. System X will send geo coordinates to the receiver service (Http Triggered Azure Function) of the middleware in JSON format.
  2. Receiver service will call reverse geocoder API and will extract city name from the response and finally sends the city name to the Service Bus queue.
  3. Sender service will receive city name from service and call weather API and send weather data to System Y (Service Bus Queue Triggered Function).
  4. System Y will receive weather information from the sender service and store it (For sake of simplicity, we will log the information at Sender Service).


  1. Microsoft Azure Subscription.
  2. Deployed Service Bus resource on Microsoft Azure Portal.
  3. Postman for testing
  4. Visual Studio 2019
  5. .NET Core 3.1

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Flo  D'Amore

Flo D'Amore


Microsoft Launches a New Communication Platform with Azure Communication Services

During its annual Ignite Conference, Microsoft announced Azure Communication Services (ACS), a fully-managed communication platform. The offering is currently in public preview.

The new Microsoft offering on Azure leverages the same network that powers Microsoft Teams. Developers can add voice and video calling, chat, and SMS text message capabilities to mobile apps, desktop applications, and websites through developer-friendly APIs and SDKs. Furthermore, it also allows developers to tap into other Azure services, such as Azure Cognitive Services for translation, sentiment analysis and more. Note that all communications between ACS, apps and websites are being encrypted to meet privacy and compliance needs, such as HIPAA and GDPR.

_Source: _

Developers can access ACS through REST APIs through the language of their choice. All APIs will need an access token, which is generated by ACS. Besides leveraging the REST APIs, developers opt to use one of the SDKs - available in .NET Core, JavaScript, Java, and Python. Furthermore, there are client SDKs for both iOS and Android. The client libraries that underpin the SDKs are a mix of open and closed source - the open versions are available on GitHub.

Other cloud vendors Google and AWS offer similar features as ACS. AWS, for example, offers several services like Amazon ConnectContact LensNotification Services and PinPoint, while Google continues to expand Contact Center AI. Furthermore, SaaS companies like Twilio and MessageBird offer a similar set of core features.

Scott Van Vliet, corporate vice president, Intelligent Communications, stated in an Azure blog post announcing ACS:

#rest #microsoft azure #communication #cloud #azure #api #architecture & design #development #devops #news

Aisu  Joesph

Aisu Joesph


Securing Microsoft Active Directory


K-means is one of the simplest unsupervised machine learning algorithms that solve the well-known data clustering problem. Clustering is one of the most common data analysis tasks used to get an intuition about data structure. It is defined as finding the subgroups in the data such that each data points in different clusters are very different. We are trying to find the homogeneous subgroups within the data. Each group’s data points are similarly based on similarity metrics like a Euclidean-based distance or correlation-based distance.

The algorithm can do clustering analysis based on features or samples. We try to find the subcategory of sampling based on attributes or try to find the subcategory of parts based on samples. The practical applications of such a procedure are many: the best use of clustering in amazon and Netflix recommended system, given a medical image of a group of cells, a clustering algorithm could aid in identifying the centers of the cells; looking at the GPS data of a user’s mobile device, their more frequently visited locations within a certain radius can be revealed; for any set of unlabeled observations, clustering helps establish the existence of some structure of data that might indicate that the data is separable.

What is K-Means Clustering?

K-means the clustering algorithm whose primary goal is to group similar elements or data points into a cluster.

K in k-means represents the number of clusters.

A cluster refers to a collection of data points aggregated together because of certain similarities.

K-means clustering is an iterative algorithm that starts with k random numbers used as mean values to define clusters. Data points belong to the group represented by the mean value to which they are closest. This mean value co-ordinates called the centroid.

Iteratively, the mean value of each cluster’s data points is computed, and the new mean values are used to restart the process till the mean stops changing. The disadvantage of k-means is that it a local search procedure and could miss global patterns.

The k initial centroids can be randomly selected. Another approach of determining k is to compute the entire dataset’s mean and add _k _random co-ordinates to it to make k initial points. Another method is to determine the principal component of the data and divide it into _k _equal partitions. The mean of each section can be used as initial centroids.

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