microsoft azure ml: machine learning as a service dmitry petukhov#moscowdatafest
TRANSCRIPT
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
Runti
me
Yet
Anoth
er
Runti
me
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
y
Python / Rtools
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 Workspace
Step 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
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 the
within-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 [email protected])
Stay connectedFacebook: @code.zombi
LinkedIn: @dpetukhovHabr: @codezombieAll contacts…
Read my tech code instinct blog
Download presentation from http://0xcode.in/moscow-data-fest or
Azure ML: Machine Learning as a Service