Azure, combined with Microsoft Cognitive Services, are a huge opportunity for developers. Has Microsoft's Cognitive services piqued your interest, but you haven't been able to find a decent course that will teach you how to use those services effectively? Or maybe you have just recognised how a valuable skill like machine learning can open up big opportunities for you as a developer. Perhaps you just wanted to find out how to add "superpowers" to your programs to do amazing things like face detection, but had no idea how to go about it. Whatever the reason that has brought you to this page, one thing is for sure; the information you are looking for is contained in this course!

The extractive summarization involves getting key phrases from the actual document and combining those key phrases to make a brief summary. Most used technique for extractive text summarization is sentence scoring. So, extractive summarization involves assigning saliency measure to some units of the documents and extracting those with highest scores to include in the summary of the document.Extractive text summarization methods can be classified into two: Supervised Learning methods and Unsupervised Learning methods.

AI is getting democratized to a great extent. Interesting times where the tool sets are evolving, and the underlying hardware component are getting purpose built. As a developer or a data scientist, you have a dream platform that lets you build systems with utmost focus on protecting people, their data and control the end to end machine learning process. If you are a developer or an ML practitioner, do not miss this power packed session with interesting demos. Session 1: Empowering developers for social cause with Microsoft Cognitive Service In the current social situation, we need to come together and behave responsibly to ensure that everyone around us is safe.

As AI Developers we understand the power that the Cognitive Services provide. The facility to build intelligent and supported algorithms into apps, websites, and bots to see, hear, speak, understand, and interpret your user needs. The APIs in Cognitive Services are hosted on a growing network of Microsoft-managed data centers. We need to start thinking on new scenarios that were simply not possible before. Smart sensors, connected devices are becoming a trend towards a future powered by the intelligent cloud and intelligent edge.

My name is Sahil Malik, and welcome to my course, Microsoft Azure Developer: Creating and Integrating AI with Azure Services. I have been talking for about 3 seconds, and in these 3 seconds YouTube has seen 15 hours of content uploaded, USPS has scanned thousands of handwritten addresses, and so many smartphones have taken pictures, cleverly adjusting contrast and brightness thanks to face recognition algorithms. The management of all this is thanks to AI in application around us. Think of how much data has your company produced in this time. Will it make you more productive? Will it make you less liable?

Artificial intelligence brought the promise of automating one of the most time-consuming and manual processes in digital asset management – asset tagging. With artificial intelligence, thousands of existing and new assets can be processed in a matter of hours, rather than weeks. But, while AI is able to suggest relevant metadata, human verification is still required to evaluate the levels of accuracy and relevance to the business. As we implement AI into a DAM, we need to be careful to do so in a way that makes asset metadata more meaningful, not less, so that DAM administrators can make assets more easily discoverable. Here are 6 best practices to help you achieve this and ensure your team successfully implements artificial intelligence in your DAM.

Artificial intelligence (AI) is rapidly transforming everything today, from daily lives to transportation to businesses. Humans have always found the concept of AI very enthralling as is evident from the number of hit sci-fi movies. Scientists and researchers have worked hard on making this technology a norm for the human beings. Enterprises are adopting AI and machine learning (ML) for various use cases, which has risen the demand for AI engines that can be used to develop intelligent applications and tools. Such apps and tools help them automate the repetitive, tedious and difficult tasks that can affect productivity and cost of operation.

The Grey Matter Managed Services team are experts at managing Azure-related projects, including Cognitive Services. You can contact them on 44 (0)1364 654100 to discuss Azure and Visual Studio options and costs, or if you require technical advice.

The blog post was authored by Andy Hickl, Principal Group Program Manager, Microsoft Cognitive Services. Today at the Build 2018 conference, we are unveiling several exciting new innovations for Microsoft Cognitive Services on Azure. At Microsoft, we believe any developer should be able to integrate the best AI has to offer into their apps and services. That's why we started Microsoft Cognitive Services three years ago – and why we continue to invest in AI services on Azure today. Microsoft Cognitive Services make it easy for developers to easily add high-quality vision, speech, language, knowledge and search technologies in their apps -- with only a few lines of code.

Microsoft Cognitive Services is home to the company's hosted artificial intelligence algorithms. Today, the company announced advances to several Cognitive Services tools including Microsoft Custom Vision Service, the Face API and Bing Entity Search . Joseph Sirosh, who leads the Microsoft's cloud AI efforts, defined Microsoft Cognitive Services in a company blog post announcing the enhancements, as "a collection of cloud-hosted APIs that let developers easily add AI capabilities for vision, speech, language, knowledge and search into applications, across devices and platforms such as iOS, Android and Windows." These are distinct from other Azure AI services, which are designed for developers who are more hands-on, DIY types. The idea is to put these kinds of advanced artificial intelligence tools within reach of data scientists, developers and any other interested parties without any of the normal heavy lifting required to build models and get results with the myriad of testing phases that are typically involved in these types of exercises.