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A software developer, specialized in Microsoft technologies 

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ChatBot

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How we   choose the bot framework ? If i talk about Microsoft. Essentially we have a single bot, is built and later rolled out to multiple canvasses without any further customization. Currently, the platform supports many channels (such as Facebook Messenger, Slack, SMS, email, Web Chat) including Microsoft’s own premiers (like Skype, Cortana, Microsoft Teams, Bing, GroupMe). All settings are pre-configured on the Developer Portal with an easy-to-follow walkthrough about how to integrate the bot with the hosting applications. How we   consume the bot framework ? Developers can connect directly from their client application, web chat controls or mobile apps throw Direct Line REST API tool of the bot Connector. This API makes it possible to empower existing applications and services with a conversational user interface. Microsoft has its own massive range of cloud-based Cognitive Services   APIs with the power of machine learning and AI algorithms. ...
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Cross-Origin Resource Sharing and Azure Machine Learning web services

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Cross-Origin Resource Sharing (CORS) is a mechanism that allows web objects (e.g., JavaScript) from one domain to request objects (web service requests) from another domain. Cross-origin requests initiated from scripts have been subject to restrictions primarily for security reasons. CORS provides a way for web servers to support cross-site access controls, which enable secure cross-site data transfers. At the time of this writing, it should be noted that Azure Machine Learning web services do not currently support CORS requests. See the following link for more information: https://social.msdn.microsoft.com/Forums/en-US/b6ddeb77-30e1-45b2-b7c1-eb4492142c0a/azure-ml-published-web-services-cross-origin-requests?forum=MachineLearning. This CORS restriction really means that if you wish to fully exploit Azure Machine Learning web services for deployment, testing, and production for a wide variety of (web) clients, you will need to host your own server-side applications. You basically hav...
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Azure Machine Learning Workflow

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The process of creating a Machine Learning on Azure  is composed of a many pattern of workflow steps. This workflow are designed to help users to create a new predictive alanytics in no limited time. The main steps in the process are summarized in this figure : Data :  Is your input, who will be acquired, compiled , analyzed, tested and trained Create the model :  There is a versious algorithms of machine learning. we should create a models who is capable to make predictions, based on interfaces about the datasets. Evaluate the model :  This step is so important in the process because we examine the accuracy of new predictive models based on ability to predict the correct outcome, when we know in advance the input and the output values. Accuracy is measured in terms of confidence factor approching the whole number one Refine and evaluate the model :  After evaluating the model we refine-it by comparing, contrasting and combining alternate...
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