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Lab: Cloud Workshop - Big Data and Visualization with Azure Databricks


In this workshop, you will deploy a web app using Machine Learning Services 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.

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 Azure 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.

  • Estimated time required to complete: 4 hours, 30 minutes
  • You will have access to this environment for 8 hours, 0 minutes
Who this lab is designed for
  • Data Professionals
  • Data Engineers
  • Data Scientists
  • Data Architects

Learning Objectives

  • This hands-on lab is designed to provide exposure to many of Microsoft's transformative line of business applications built using Microsoft big data and advanced analytics.
  • By the end of the lab, you will be able to show an end-to-end solution, leveraging many of these technologies, but not necessarily doing work in every component possible.


Before the Lab
You should follow all the steps provided in the Before the Hands-on Lab section to prepare your environment before attending the hands-on lab. Failure to complete the Before the Hands-on Lab setup may result in an inability to complete the lab with in the time allowed.
Hands-on Lab

Margie's Travel (MT) provides concierge services for business travelers. In an increasingly crowded market, they are always looking for ways to differentiate themselves, and provide added value to their corporate customers.

They are looking to pilot a web app that their internal customer service agents can use to provide additional information useful to the traveler during the flight booking process. They want to enable their agents to enter in the flight information and produce a prediction as to whether the departing flight will encounter a 15-minute or longer delay, considering the weather forecasted for the departure hour.

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