Course 40502C: Microsoft Cloud Workshop: Big Data & Visualization

In development
Publication date: 30 May 2018

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Anytime accessNo
Anywhere access to recorded instructorNo
Microsoft official training contentYes
In-depth trainingYes
Hands-on labs No
SATV redemption Yes
Ask instructor questions in personYes
Attend live class in personYes
Attend live class remotelyYes
Time commitment
About this course
Technology:Data Platform
Level:Advanced
This Revision:C
Length:1 day
Language(s):English

Publication date:

30 May 2018
Overview
About this course
Complete a web app using Machine Learning to predict travel delays given flight delay data and weather conditions. Plan the bulk data import operation, followed by preparation, such as cleaning and manipulating the data for testing, and training your Machine Learning model.
Audience profile
This workshop is intended for Cloud Architects and IT professionals who have architectural expertise of infrastructure and solutions design in cloud technologies and want to learn more about Azure and Azure services as described in the ‘About this Course’ and ‘At Course Completion’ areas.  Those attending this workshop should also be experienced in other non-Microsoft cloud technologies, meet the course prerequisites, and want to cross-train on Azure.
At course completion

Students will be better able to build a complete Azure Machine Learning (ML) model for predicting if an upcoming flight will experience delays. In addition,

  • Integrate the Azure ML web service in a Web App for both one at a time and batch predictions
  • Analyze batch data with SQL Data Warehouse
  • Visualize batch predictions on a map using Power BI
Course details
Course OutlineModule 1: Whiteboard Design Session - Big Data and VisualizationLessons
  • Review the customer case study
  • Design a proof of concept solution
  • Present the solution
Module 2: Hands-On Lab - Big Data and VisualizationLessons
  • Build a Machine Learning Model
  • Setup Azure Data Factory
  • Develop a data factory pipeline for data movement
  • Operationalize ML scoring with Azure ML and Data Factory
  • Summarize data using HDInsight Spark
  • Visualizing in Power BI Desktop
  • Deploy intelligent web app
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