Collaborating Authors



Retrieving information from documents and forms has long been a challenge, and even now at the time of writing, organisations are still handling significant amounts of paper forms that need to be scanned, classified and mined for specific information to enable downstream automation and efficiencies. Automating this extraction and applying intelligence is in fact a fundamental step toward digital transformation that organisations are still struggling to solve in an efficient and scalable manner. An example could be a bank that receives hundreds of kilograms of very diverse remittance forms a day that need to be processed manually by people in order to extract a few key fields. Or medicinal prescriptions need to be automated to extract the prescribed medication and quantity. Typically organisations will have built text mining and search solutions which are often tailored for a scenario, with baked in application logic, resulting in an often brittle solution that is difficult and expensive to maintain.

Artificial Intelligence - Getting Started with Microsoft AI


Software developers are quickly adopting Artificial Intelligence (AI) technologies, such as natural language understanding, sentiment analysis, speech recognition, image understanding and machine learning (ML). Across a broad range of industries and sectors, AI-infused software applications and cloud services drive innovative customer experiences, augment human capabilities and transform how we live, work and play. New tools, cloud-hosted APIs and platforms make it even easier to build such applications. Modern AI applications live at the intersection of cloud computing, data platforms and AI tools. The cloud provides a powerful foundation for elastic compute and storage, while supporting special-purpose hardware such as graphics processing units (GPUs) that accelerate demanding calculations.

Announcing tools for the AI-driven digital transformation


Artificial Intelligence (AI) has emerged as one of the most disruptive forces behind the digital transformation of business. Today, at Microsoft Ignite 2017, as we engage in conversations about digital transformation with over 25,000 customers and partners, I am pleased to share some of our latest and most exciting innovations in AI development platforms. These announcements – which span Azure Machine Learning (AML), new Visual Studio tools for AI, Cognitive Services and new enterprise AI solutions – demonstrate our mission to bring AI to every developer and every organization on the planet, and to help businesses augment human ingenuity in unique and differentiated ways. Today we are announcing a set of powerful new capabilities in AML for developers to exploit big data, GPUs, data wrangling and container based model deployment. Let me tell you more about these below and for a deep dive please review this AML blog.

How Azure Percept Simplifies Building And Deploying AI Models At Edge


Azure Percept is the latest edge computing platform from Microsoft. Announced at the recent Ignite event, the platform brings the best hardware, software and cloud services to the edge. Azure Percept is an exciting device for makers and builders to build and prototype intelligent IoT applications powered by Azure Cognitive Services and Azure Machine Learning Services. The Azure Percept platform has three elements - the hardware, development kit, and cloud-based development and management tools. Microsoft is working with the ecosystem of hardware developers to publish patterns and best practices for developing edge AI hardware that can be integrated easily with Azure AI and IoT services.

10 Most Interesting Announcements From Microsoft Build


Microsoft's annual developer conference, Build is attended by thousands of developers physically and virtually. As expected, Microsoft has made a slew of announcements related to Windows, Visual Studio, Azure, Xbox, HoloLens and more. Here are the most exciting announcements related to cloud, containers, DevOps, IoT and edge computing. Microsoft and Red Hat collaborated to build a light-weight, event-driven scaling extension to Kubernetes called as Kubernetes-based Event Driven Autoscaling (KEDA). The KEDA extension can run on any Kubernetes environment to scale-in and scale-out workloads based on external parameters such as the number of messages in a queue.