The terms “Modern Workplace” or “Digital Workplace” is something every one of us have heard about in the last couple of months probably. It is some trend which should improve the workplace situation in order to digitally transform the way we work and make it more easier to communicate and collaborate with others. Therefore, the use of smart services can be beneficial when it comes to really transforming your workplace into a modern one.

With the announcement of the GA status of the Language Understanding Intelligent Service a couple of weeks ago, I was thinking about how to easily enable people in using this awesome Cognitive Service. I was asked many times on how to easily use that service but I faced a lot of open questions and confusion. That’s why I decided to create a cheat sheet to illustrate the process of creating, training and publishing a LUIS model in order to use it within a Bot or other apps which should act intelligent and understand the human language.

“Artificial intelligence productivity for every developer and every scenario” - this is the slogan which is shown on the landing page of the Microsoft AI Platform . But many people don’t know which services are included in this broad AI ecosystem. It is not only about Bots or Machine Learning - it’s about much more like Cognitive Services, it’s about the tools used for developing AI apps and the frameworks underneath all those great and intelligent services.

Yesterday I wanted to deploy a new bot with the Azure Bot Service to start developing a new digital assistant within the Azure Bot Framework platform. But as I created my bot from the Azure portal I ran into a strange error upon testing the bot immediately, which you can see below: As you can see the bot’s function started, but it didn’t finish successfully and in the test window I got the error message “couldn’t send retry” which was quite strange as I didn’t modify the code at all.

Microsoft’s Bot Framework is a great platform for designing, developing and deploying bots for various use cases. As nowadays many people are familiar with bots and they get used to connect and communicate with bots via social media, it’s a good opportunity to bring a service to the next level by adding a bot to it for a better communication service. In the past, t was very hard to achieve that as you would basically have to develop the bot on your own and connect all the different systems and platforms together to make the bot available to a broader audience.

One of the coolest Cognitive Services APIs is finally generally available: the Text Analytics API. If you have a look at **Survey 365 **you will see what awesome stuff the Text Analytics API lets you do. Like in this example, you can extract text and detect the sentiment score of a given text to see if it is rather positive or negative. And all that without coding a lot. You can see the official announcement from the Microsoft Azure team about the GA information below: