Course 40502-G: Microsoft Cloud Workshop: Big Data & Visualization

Course Modality

Instructor-led (classroom)

Course Level

Advanced

Course Time

1 day

Course Language

English

Course Overview

In this workshop, you will deploy a web app using Machine Learning (ML) to predict travel delays given flight delay data and weather conditions. Plan a bulk data import operation, followed by preparation, such as cleaning and manipulating the data for testing, and training your Machine Learning model.

Prerequisites

There are no prerequisites for this course.

Intended Audience

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.

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Self-Paced Online Video

A 360-degree learning approach that you can adapt to your learning style

A 360-degree learning

Engage and learn more with these live and highly-interactive classes alongside your peers

24/7 Teaching Assistance

24/7 Teaching Assistance Keep engaged with integrated teaching

Online Practice Labs

Projects provide you with sample work to show prospective employers.

Applied Projects

Real-world projects relevant to what you’re learning throughout the program

Learner Social Forums

A support team focused on helping you succeed alongside a peer community

Skill Covered

  • At the end of this workshop, you will be better able to build a complete machine        learning model in Azure Databricks for predicting if an upcoming flight will                experience delays. In addition, you will learn to store the trained model in Azur    Machine Learning Model Management, then deploy to Docker containers for              scalable on-demand predictions, use Azure Data Factory (ADF) for data              movement and operationalizing ML scoring, summarize data with Azure                    Databricks and Spark SQL, and visualize batch predictions on a map using Power BI.

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Course Curriculum

Whiteboard Design Session – Big data and visualization

Lessons

  • Review the customer case study
  • Design a proof of concept solution
  • Present the solution

Hands-on Lab – Big data and visualization

Lessons

  • Build a Machine Learning Model
  • Setup Azure Data Factory
  • Deploy your machine learning model with Azure ML
  • Develop a data factory pipeline for data movement
  • Operationalize ML scoring with Azure Databricks and Data Factory
  • Summarize data using Azure Databricks
  • Visualizing in Power BI Desktop
  • Deploy intelligent web app

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