Transcript Document

Azure Machine Learning
Introduction to Azure ML
Setting Expectations
This presentation is for you if…
 you hear the buzzword “Machine Learning” and
want a better understanding of what it is
 you want to know how to get started building your
first experiment using Azure ML Studio
This presentation is NOT for you if…
 you already completed Microsoft Virtual Academy
and Quick Start offerings related to Azure ML
 you already created and published your own
machine learning projects
Agenda
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Why Machine Learning
What is Machine Learning
Machine Learning in Azure
Hands On: Azure ML Studio
Questions
Agenda
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Why Machine Learning
What is Machine Learning
Machine Learning in Azure
Hands On: Azure ML Studio
Questions
The United States Postal Service
processed over 150 billion pieces of
mail in 2013—far too much for
efficient human sorting.
But as recently as 1997, only 10% of
hand-addressed mail was
successfully sorted automatically.
The challenge in automation is
enabling computers to interpret
endless variation in handwriting.
More than just mail circulating…
Agenda
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Why Machine Learning
What is Machine Learning
Machine Learning in Azure
Hands On: Azure ML Studio
Questions
Using past data to predict the future
Imagine what machine
learning could do for
your business.
Churn
analysis
Ad
targeting
Image
detection &
classification
Equipment
monitoring
Recommendations
Forecasting
Spam
filtering
Fraud
detection
Anomaly
detection
Using past data to predict the future
Ad
targeting
Image
detection &
classification
Equipment
monitoring
Recommendations
Forecasting
Spam
filtering
Fraud
detection
Anomaly
detection
Churn
analysis
• XBOX Halo
Imagine what machine
learning could do for
your business.
Using past data to predict the future
Imagine what machine
learning could do for
your business.
Churn
analysis
Ad
targeting
Image
detection &
classification
Equipment
monitoring
Recommendations
Forecasting
Spam
filtering
Fraud
detection
• Shopping
Basket Analysis
Anomaly
detection
Using past data to predict the future
Imagine what machine
learning could do for
your business.
Churn
analysis
Ad
targeting
Image
detection &
classification
Equipment
monitoring
Recommendations
Forecasting
Spam
filtering
Fraud
detection
Anomaly
detection
• Credit Card
Using past data to predict the future
Imagine what machine
learning could do for
your business.
Churn
analysis
Ad
targeting
Image
detection &
classification
Equipment
monitoring
Recommendations
Forecasting
Spam
filtering
Fraud
detection
Anomaly
detection
• Health / Medical
Techniques for solving?
Classification
Regression
Clustering
(Recommenders)
Anomaly
Detection
Types of machine learning
 Supervised Learning
 Used when you want to
find unknown answers
and have data with
known answers
 Train model using test
data with known
outcomes
 Measure effectiveness of
various algorithms’
prediction against known
outcomes in test set
 Publish best trained
model to predict
outcomes for new inputs
 Unsupervised Learning
 Used when you want to
find unknown answers –
mostly groupings –
directly from data
 No simple way to
evaluate accuracy
 Apply algorithm
 Evaluate groups
Sample – Supervised Learning
Start with a question:
Which customers will buy a bike?
Sample – Supervised Learning
Analyze historical data set that includes predictive
attributes and known answer.
=
5 mile commute
2 kids
Sample – Supervised Learning
Analyze historical data set that includes predictive
attributes and known answer.
=
15 mile commute
1 kid
Separate into Training and Test sets
Training
Test
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Why Machine Learning
What is Machine Learning
Machine Learning in Azure
Hands On: Azure ML Studio
Questions
Challenges prior to Azure ML
Hard-to-reach solutions
Expensive
Huge set-up costs of tools, expertise, and
compute/storage capacity create unnecessary
barriers to entry
Siloed
data
Siloed and cumbersome data management
restricts access to data
Fragmented
tools
Complex and fragmented tools limit
participation in exploring data and
building models
Deployment
complexity
Many models never achieve business
value due to difficulties with deploying
to production
Break away
from industry
limitations
Introducing Azure Machine Learning
Accessible solutions
Cloud-based
Minimal set-up costs with ability to easily scale
compute/storage capacity; fewer barriers to
entry
Data
Integration
Easy to integrate data from various data
sources
Common
Toolset
Users can collaborate in common toolset to
build and train models using advanced
algorithms (also supports existing R and
Python assets)
Deployment
simplicity
Easy to deploy trained models as consumable
web services
Agenda
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Why Machine Learning
What is Machine Learning
Machine Learning in Azure
Hands On: Azure ML Studio
Questions
http://azure.microsoft.com/en-us/services/machine-learning/
Steps to build a ML Solution
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Why Machine Learning
What is Machine Learning
Machine Learning in Azure
Hands On: Azure ML Studio
Questions
Contact Info
 Scott Hietpas
 [email protected]
 http://www.linkedin.com/pub/scotthietpas/2/119/189
 Adam Widi
 [email protected]
 http://www.linkedin.com/pub/adamwidi/15/5aa/499