Uber is using Microsoft Cognitive Services to offer Real-Time ID Check. Drivers verify their identity using selfies before they are able to accept rides. Learn more at https://www.microsoft.com/cognitive-services/
In this tutorial, Drew DiPalma will walk you through the steps to connect Azure Machine Learning to Azure SQL Data Warehouse.
In this episode, Robert is joined by Harikrishna Menon, who shows us MyDriving, an Azure IoT and Mobile sample application. This solution uses On-board diagnostics (OBD) data from your car to analyze your driving. The backend uses multiple Azure Services like IoT Hub, Stream Analytics, SQL databases, HDInsight, Machine Leaning, and App Services and how you can configure them without having to write a lot of code. The app runs on Windows, iOS and Android and is built with Xamarin. Hari shows how to make use of the full DevOps cycle (Visual Studio Team Services, HockeyApp, Xamarin Test Cloud) to build, test and distribute the app.
Azure Machine Learning API service enables you to deploy predictive models build in Azure Machine Learning studio as scalable, fault tolerant Web services. Azure ML API service leverages Microsoft Azu
Data Access is the first step of data science workflow. Azure Machine Learning supports numerous ways to connect to your data. This video illustrates several methods of data ingress in Azure Machine Learning.
The Microsoft Azure ML team recently announced the availability of 3 ML templates on the Azure ML Studio – for online fraud detection, retail forecasting and text classification. These templates demonstrate industry best practices and common building blocks used in an ML solution for a specific domain, starting from data preparation, data processing, feature engineering, model training to model deployment (as a web service) . The goal for Azure ML templates is to make data scientists more productive and faster in building and deploying their custom ML solutions on the cloud. Templates include a collection of pre-configured Azure ML modules as well as custom R scripts in the Execute R Script modules to enable an end-to-end solution. We'll walk through these templates in detail in this and future webinars. In this webinar, we focus on the Text Classification template. There are broad applications of text classification: categorizing newspaper articles and news wire contents into topics, organizing web pages into hierarchical categories, filtering spam email, sentiment analysis, predicting user intent from search queries, routing support tickets, and analyzing customer feedback. The goal of text classification is to assign some piece of text to one or more predefined classes or categories. The piece of text could be a document, news article, search query, email, tweet, support tickets, customer feedback, user product review etc. This template demonstrates how to do text processing, feature engineering, training a sentiment classification model, and publishing it as a web service using twitter sentiment dataset.
Azure Stream Analytics removes the complexity of stream processing for developers by providing a SQL-like language for authoring queries. Learn how to build queries to perform data transformations such as filtering, aggregations over time windows, joining multiple streams together, correlating reference data (or static) with streaming data, and detecting patterns over data streams in real time. With live code demos over an Internet of Things (IoT) scenario, gain the knowledge needed to create your first stream processing job in minutes.
Mit Azure Machine Learning bietet Microsoft die Möglichkeit, Machine Learning in der Cloud auszuführen und mit anderen Azure-Diensten zu integrieren. In diesem Webinar präsentieren wir Ihnen folgende
Gaining attention largely due to the rise of the Internet of Things (IoT), predictive maintenance can be defined as a technique to predict when an in-service machine will fail so that maintenance could be planned in advance. However, the concept of predictive maintenance has evolved and covers a wide range of applications. Through a real-world example, I will show different ways of formulating a failure prediction problem. By showing a step-by-step procedure of data input, data preprocessing, data labeling and feature engineering to prepare the training/testing data on a publicly available dataset, I will present how convenient it is to build a predictive model in Azure ML and deploy it as a web service. At the end of this session, the audience is expected to understand the landscape and challenges of predictive maintenance applications, and also understand how to make usage of the predictive maintenance template offered by Microsoft Azure ML.
The video shows the user how to consume an Request Response web service from R Studio using the published API's R sample code. Visit Machine Learning Documentation to learn more.