Microsoft Azure ML:Machine Learning as a

Service

Dmitry Petukhov #MoscowDataFest

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Storage

ResourceManagement

ML Framework

Execution Engine

Local OS

Local Disc

Pyth

on

Runt

ime

Yet A

noth

er

Runt

ime

scikitlearn

HDFS

YARN

MapReduce

Mahout

HDFS / S3

YARN / Apache Mesos

Spark

MLlib

HDFS / S3

YARN / Apache Mesos

Python / R on Spark

Python / Rtools

Spark

Distributed FS

Dark Magic…

Local PC Hybrid Model Cluster (on-premises/cloud)ML as a Service

(cloud)

Challenge

somelibrar

yPython / R

tools

Intro <- function() {Hello Data Fest!I need your help

}

Learn <- function() {Azure ML Overview # +Hello Azure ML DemoData Science Workflow vs Azure ML

}

Code <- function() {ML Skills Cluster Analysis # Demo 1Twitter sentiment analysis # Demo 2

}

Coffee <- function() {Q&AContacts

}

Agenda

Dmitry Petukhov,Software Architect + Developer,Microsoft Certified Professional (C#),Big Data Enthusiast && Coffee Addict

Researcher & Developer @ OpenWay

Hello Data Fest!

Azure Machine Learning. Introduction

Guiding Principles

Reduce complexity to broaden participationNo software to install, only web browser;Possibility to develop without writing line of code;Easy deployment and usage using restfull API;Easy collaboration on Azure ML projects;Visual composition with end2end support for Data Science

workflow;Extensible, support for R OSS.

Data Science is far too complex todayMathComputer ScienceDomain

Reference: TechEd 2014 Conference

Azure Machine Learning. Overview

Data Azure Machine Learning

Consumers

Local storageUpload data from

PC…

Cloud storageAzure StorageAzure TableHiveetc.

Excel

Business Apps

Reference: TechEd 2014 Conference

Azure Machine Learning. Overview

Business problem Modeling Business valueDeployment

Azure Marketplace(Applications

store)Azure ML Gallery

(community)

ML Web Services(REST API Services)

ML Studio(Web IDE)

Workspace:ExperimentsDatasetsTrained modelsNotebooksAccess settings

Data Model API

Manage API

Step 3. Create Azure ML WorkspaceStep 4. Go to Azure ML Studio &create ML Experiment Step 5. Publish result

Azure Machine Learning. Overview

Demo #0:Hello Azure ML!

Step 1. Get $200 creditSign up for Azure free trial.

Step 2. Get access to Azure Portal

Azure Machine Learning. Azure ML Flow

Supervised Learning FlowPart #1

Azure Machine Learning. Azure ML Flow

Supervised Learning FlowPart #2

Source

Azure Machine Learning. Azure ML Flow Source: Azure ML Cheat Sheet

Demo #1:ML Skills Cluster

Analysis

Azure Machine Learning. Demo

k-means clustering aims to partition the n observations into k (≤ n) sets S = {S1, S2, …, Sk} so as to minimize thewithin-cluster sum of squares (WCSS).

where (x1, x2, …, xn) – observations, μi is the mean of points in Si.

Source: Wikipedia

Demo #2:Twitter sentiment

analysis

Azure Machine Learning. Demo

TD-IDF, short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus.

Source: Wikipedia

Restrictions

Legislative restrictionsInternational & local

Azure platform restrictionsMax storage volume per account, etc.

Azure ML service restrictionsData

Max dataset volume: 10 GbVector size limitation: 2^64

Throttled policy 20 concurrent request per endpointMax endpoints count: 10K

Black boxNo debugNo Scala, C++, C# No your own right algorithms

Azure Machine Learning. Conclusion

Killer Features

R (quickstart)Support R models & scripts

Python (quickstart)Support Python scriptsJupyter Notebooks in Azure ML Studio

PublishingREST API & real-time mode vs batch-mode

Azure ML GalleryShare for community

Azure MarketplaceSaaS store

In-the-box integration with…Hive, Azure Storage, Excel, Cortana Analytics Stack

Free Start & it’s child age

Azure Machine Learning. Conclusion

Nothing has changed

Reduce complexity to broaden participationNo software to install, only web browser;Possibility to develop without writing line of code;Easy deployment and usage using restfull API;Easy collaboration on Azure ML projects;Visual composition with end2end support for Data Science

workflow;Extensible, support for R OSS.

Data Science still too complex today

MathComputer ScienceDomain

Azure Machine Learning. Conclusion

References

Start from azure.com/ml

Microsoft Machine Learning Blog

Azure ML documentation +free online course, videos & books

Microsoft Research: Azure for Researchers

Azure Machine Learning. Conclusion

© 2015 Dmitry Petukhov All rights reserved. Microsoft Azure and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.

Thank you!

Q&ANow or later (send on d.petukhov@outlook.com)

Stay connectedFacebook: @code.zombi

LinkedIn: @dpetukhovHabr: @codezombieAll contacts…

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Azure ML: Machine Learning as a Service