Data Science in the cloud withMicrosoft AzureMARTIN THORNALLEYDATA SOLUTION ARCHITECT, MICROSOFT

Introduction

Data Science Definition

“Data science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, machine learning, data mining, and predictive analytics”https://en.wikipedia.org/wiki/Data_science

Data Science Skillset

http://berkeleysciencereview.com/how-to-become-a-data-scientist-before-you-graduate/

The Cloud

Why does the Cloud matter for Data Science?

High capacity and cost effective data storage Flexible, elastic compute capacity Ready to use technologies Choice of Infrastructure or Platform Enables Agile & DevOps Operational reliability and security Pay as you go

Microsoft Azure Cloud Platform

Wide range of services covering Compute, Web & Mobile, Data & Storage, Analytics, Internet of Things & Intelligence plus many more, see http://azureplatform.azurewebsites.net/en-us/

Easy to get started, free to try for 30 days but limited spend, also MSDN licence free credits, see https://azure.microsoft.com/en-gb/free/

Comprehensive documentation and examples Global presence with many recognisable brands fully

committed Huge investment and growing rapidly

Data Science Process

https://azure.microsoft.com/en-us/documentation/articles/data-science-process-overview/

Worked Example

NYC taxis

2013 NYC taxi trips and fares – open but non-trivial dataset 24 CSV files - 12 trip, 12 fare, 1 for each month ~20GB compressed, ~50GB uncompressed, 170+ million records

medallion – vehicle identifier hack license – driver identifier passenger count pickup & dropoff – datetime, longitude, latitude trip – time and distance fare - payment type, fare amount, surcharge, mta tax, tip amount,

tolls amount, total amounthttp://www.andresmh.com/nyctaxitrips/

Predictions

Predict whether a specific journey will result in a tip – binary classification

Predict what class of tip will be for a specific journey – multiclass classification

Predict how much a tip will be for a specific journey – regression

A Data Science Environment

Data Science Virtual Machine

Create Linux and Windows virtual machines in minutes Wide range of configurations - CPU cores, memory, disks,

network speeds Scale to what you need Pay only for what you use Enhance security and compliance Preloaded with full set of tools and utilities from Azure

MarketPlace e.g. SQL Server 2016 Developer edition, Azure SDK, Python, R, Jupyter, etc.

Storage Accounts

Massively scalable cloud storage for your applications Security-enhanced, durable, and highly available across the

globe Industry-leading performance with exabytes of capacity Pay only for what you use Open, multi-platform support

HDInsight

A managed Apache Hadoop, Spark, R, HBase, and Storm cloud service made easy Scale to petabytes on demand Crunch all data—structured, semi-structured, unstructured Skip buying and maintaining hardware Spin up Apache Hadoop, Spark, and R clusters in the cloud Use Excel or your favourite BI tool to visualize Hadoop data Connect on-premises Hadoop clusters with the cloud

Azure Machine Learning

A fully managed cloud service that enables you to easily build, deploy, and share predictive analytics solutions. Powerful cloud based analytics, now part of Cortana

Intelligence Suite Azure Machine Learning Studio includes hundreds of built-in

packages and support for custom code Share your solution with the world in the Gallery or on the

Azure Marketplace

The Process

Preparation & Exploration

Copy data using Azcopy and decompress Inspect files and load in to RStudio Create external Hive tables and load Query over full dataset for further exploration Remove erroneous data e.g. passenger numbers, lat/long Engineer features using Hive

Distance from start to finish using Haversine calculation Binary indicator for tips Tip level based on ranges for multiclass classification

Downsample dataset and save as internal table for Machine Learning

Machine Learning & Deployment

Import Data using Hive Query Build Training Experiments Evaluate model performance Create Predictive Experiments Publish Web Service Test Web Service Call from Excel

Next Steps

To build a fully fledged enterprise solution with regular data ingestion and model execution consider the following: Data Catalog Data Factory Event Hubs & Stream Analytics Power BI Cognitive Services

Conclusion

Summary

Microsoft Azure provides a wide range of technologies for Data Science activities

Platform services reduce the management overhead No capacity limitations and flexible provisioning – pay as you go Choice of Open Source and Microsoft – use the best tool for the

task The tools are well integrated Azure Machine Learning makes it trivial to deploy your models It’s quick and easy to get started

Getting Started

Sign up for freehttps://azure.microsoft.com/en-gb/free/

Create a Data Science VMhttps://azure.microsoft.com/en-us/marketplace/partners/microsoft-ads/standard-data-science-vm/

Visit Cortana Intelligence Gallery

https://gallery.cortanaintelligence.com/

Q&A

Thank You

Martin ThornalleyData Solution Architect, Microsoft

@mthornalmartin.thornalley@microsoft.comhttps://www.linkedin.com/in/martinthornalley