Big data pipelines can span many cloud and on-premises storage and compute resources and have many complex scheduling dependencies. It can be hard to monitor and troubleshoot all of the different activities you may have running with Azure Data Lake, Azure SQL Data Warehouse, HDInsight and other services. In this webinar, see the brand new Azure Data Factory application. Learn how to deploy, monitor and manage complex data pipelines with one simple interface and consistently deliver refined data to feed your BI and other analytics tools.
In this tutorial, Drew DiPalma walks through setting up an Azure Data Factory pipeline to load data into Azure SQL Data Warehouse.
In today's fast-paced world, mobile phone customers have many choices and can easily switch between service providers. Improving customer attrition rates and enhancing a customer's experience are valuable ways to reduce customer acquisition costs and maintain a high-quality service. Many industries, including mobile providers, use Churn Models to predict which customers are most likely to leave, and to understand which factors cause customers to stop using their service. In this session, learn how to build a real-life churn model with Azure Machine Learning, make it enterprise-ready with Azure Data Factory, and deliver data insights with Power BI.
Azure Stream Analytics, is an Azure Service that enables real-time insights over streaming data from devices, sensors, infrastructure, and applications. We recently announced General Availability of this service. In this webinar, we provide introduction to the service, common use cases, example customer scenarios, business benefits, demo of how to get started and quick build a simple real time analytic application.
In this session Dan Grecoe explains Azure Machine Learning through a comprehensive end-to-end example that he builds during the session and that encompasses: •Problem detection •Algorithm selection •Machine learning model creation and deployment as a RESTful web service •Consumption of the machine learning model The session is intended for engineers, and Dan himself is an engineer, so he does not delve into a deep understanding of complex mathematical models behind machine learning, but instead focuses on the concepts of machine learning to demystify cloud 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.
The data landscape is more varied than ever with unstructured and structured data originating from many cloud and on-premises sources. Data Factory enables you to process on-premises data like SQL Server, together with cloud data like Azure SQL Database, Blobs, and Tables. These data sources can be composed, processed, and monitored through simple, highly available, fault-tolerant data pipelines. Combining and shaping complex data can take more than one try to get it right, and changing data models can be costly and time consuming. Using Data Factory you can focus on transformative analytics while the service 'takes care of the plumbing'. This session will help you jumpstart on understanding Data Factory capabilities, and the scenarios where Data Factory can be applied.
Energy demand forecasting is an essential component of the energy industry. As an example, the electrical grid relies on an accurate demand forecast to support power generation planning, enable smart strategy on price bidding and to optimize the grid operation. However, building a reliable end to end forecasting solution has always been a challenge. From data collection, storage, model building, and data flow automation each step requires massive resource and time investment. Microsoft Azure provides various services that enable you to address this challenge in a faster and cost efficient way. In this session, you will learn about how we use Azure Stream Analytics to collect real time data; Azure SQL to store data; Azure Machine Learning to build a forecast model; Azure Data Factory to automate the model and PowerBI to visualize results on a dashboard. At the end of the session, the audience would have the knowledge to create an end to end energy forecasting solution.
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.
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.