Predictive Modelling with Azure Machine Learning (Azure ML)

1. Predictive Modelling with Azure ML
Koray Kocabas

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Sponsors
Gold Sponsors
Main Sponsor

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Agenda
 3 Trends about Future
 What’s IoT
 Introduction to Big Data
 Welcome to Data World!
 Machine Learning Overview
 AzureML Strengths
 Microsoft Azure Overview
 Setting Up An AzureML Workspace
 Exploring AzureML Studio
 Azure ML API Services

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Target Audience
 Data Professional
 Developer
 Business Intelligence
 Trend Hunter
 Programming
 Probability / Statistics
 Calculus
 Database
Minimal Prerequsities

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Internet Of
Things
Big Data
Predictive
Analysis
3 Trends about Future

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"Internet Of Thing" connects the physical world to the Internet
Within 6 years, 60.000 new
"things" will be added to the
Internet every 1 second!!!
212 billion devices will be part
of the Internet of Things by
2020 (IDC)
IDC predicts that IoT will
generate nearly $9 Trillion in
annual sales by 2020

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IoT starts to be more visible in our everyday lives.
A Car produced in 2014 has an average of 60 - 100 built-
in sensors
The Internet of Things isn't coming soon. It's already
here...
IoT will soon be everywhere. Like that:
• Home (Consumer)
• Fashion (Style)
• Transport (Traffic)
• Retail (Inventory)
• Logistic (Real Time)
• Manufacturing (M2M)
• Cities (Industry)
• Health (Body)
• Environment (Prevention)
• Building (Infrastructure)
• Agriculture (Control)
• Security (Detection)

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IoT data is meaningful? 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What is Big Data?
 Data that is too large or complex for analysis in
traditional relational databases
 Typified by the “3 V’s”:
 Volume – Huge amounts of data to process
 Variety – A mixture of structured and unstructured data
 Velocity – New data generated extremely frequently
Web server log reporting Social media sentiment analysis Sensor anomaly detection

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Big Data Technologies
 Hadoop
 Open source distributed data processing cluster
 Data processed in Hadoop Distributed File System
(HDFS)
 Related projects
 Hive
 Pig
 HCatalog
 Oozie
 Sqoop
 Others
HDFS
Name Node Data Nodes
Hadoop Cluster

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Map Reduce
1. Source data is divided
among data nodes
2. Map phase generates
key/value pairs
3. Reduce phase
aggregates values for
each key
Lorem ipsum sit amet magma sit elit
Fusce magna sed sit amet magna
Key Value
Lorem 1
ipsum 1
sit 1
amet 1
magma 1
sit 1
elit 1
Key Value
Fusce 1
magma 1
sed 1
sit 1
amet 1
magma 1
Key Value
Lorem 1
ipsum 1
sit 3
amet 2
magma 3
elit 1
Fusce 1
sed 1
MAPREDUCE

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Elements

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1980
(«Neuro»)
1990
(«Symbolic»)
2000
(«Kernel Machines»)
2005
(«Graphical Models»)
2011
(«Big Data, DNN»)
Neural
Networks
Artificial
Intelligence
Learning =
Adaptation of
Neurons
based on
External
Stimuli
Expert
Systems
Decision
Tree
Learning
Learning =
Methods to
automaticaly
build Expert
Systems
Statistical Learning
Theory
Scoring Systems
Learning =
Optimization of
Convex Functions
Wide application in
products
Statistical Modeling
of Data
Learning =
Parameter
Estimation or
Inference
Distributed
computing and
storage
Deep Neural
Networks
Learning = Scalable,
Adaptive,
Computation for
Various Big Data
History

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Azure
Machine
Learning
allows us to
Solve extremely hard problems better
Extract more value from Big Data
Drive a shift in Data Analytics
The goal of machine learning is to program computers to use example data or past
experience to solve a given problem - Introduction to Machine Learning, 2nd
Edition, MIT Press

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• Accessible through a web browser, no
software to install
• Collaborative, work with anyone,
anywhere via Azure workspace
• Visual composition with end2end
support for data science workflow
• Extensible, support for R OSS

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ML
Marketplace
ML
Operationalization
ML Studio
ML Algorithms

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ML Studio
Data Scientist
Azure Portal &
ML API Service
Azure Ops
Team
ML API Services Developer

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Machine Learning & Predictive Analytics
are core capabilities that are needed
throughout your business

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Buraya Seatte görseli gelecek

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Demo...

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Thank you