Cloud Workshop - Big Data and Visualization with Azure Databricks
3 h 20 m
Lab Overview
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.

Related Learning Path(s):
Implementing Azure DataBricks
  • 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.
In this exercise, you will retrieve your Azure Storage account name and access key and your Azure Subscription Id and record the values to use later within the lab. You will also create a new Azure Databricks cluster.
In this exercise, you will implement a classification experiment. You will load the training data from your local machine into a dataset. Then, you will explore the data to identify the primary components you should use for prediction, and use two different algorithms for predicting the classification. You will then evaluate the performance of both algorithms and choose the algorithm that performs best. The model selected will be exposed as a web service that is integrated with the optional sample web app at the end.
In this exercise, you will create a baseline environment for Azure Data Factory development for further operationalization of data movement and processing. You will create a Data Factory service, and then install the Data Management Gateway which is the agent that facilitates data movement from on-premises to Microsoft Azure.
In this exercise, you will create an Azure Data Factory pipeline to copy data (.CSV files) from an on-premises server (your machine) to Azure Blob Storage. The goal of the exercise is to demonstrate data movement from an on-premises location to Azure Storage (via the Integration Runtime).
In this exercise, you will extend the Data Factory to operationalize the scoring of data using the previously created machine learning model within an Azure Databricks notebook.
In this exercise, you will prepare a summary of flight delay data using Spark SQL.
In this exercise, you will create visualizations in Power BI Desktop.
In this exercise, you will deploy an intelligent web application to Azure from GitHub. This application leverages the operationalized machine learning model that was deployed in Exercise 1 to bring action-oriented insight to an already existing business process.
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