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Machine Learning - why the hype and how it does its magic
1. Machine Learning
Why the hype & how it does its magic
DevNexus Atlanta
By: Amir Charania on Feb 23rd 2017
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Agenda
Machine Learning – Why the Hype?
Machine Learning - Fundamentals
Machine Learning - Framework and Process
Machine Learning in Action! Model Building, testing,
deploying
Machine Learning Use Cases
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Source: http://www.nytimes.com/2004/03/01/business/microsoft-amid-dwindling-interest-talks-up-computing-as-a-career.html?_r=0
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Thought leaders like Gartner identify it as a trend
Gartner defines a strategic technology
trend as one with the potential for
significant impact on the organization.
Factors that denote significant impact
include a high potential for disruption to
the business, end users or IT, the need for
a major investment, or the risk of being
late to adopt
Gartner Top 10 Strategic
Technology Trends 2016
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Gartner 2015 – Hype Cycle
Machine Learning
It’s made it to the Gartner Hype Cycle
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And the hype increased in 2016!
Machine Learning
Gartner 2016 – Hype Cycle
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Major players have launched ML platforms
Less than a year ago!
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• Increasing data volumes
• Low storage costBig Data
• Zero startup (hardware & software) cost
• Reduced startup time
• Pay for what you use
• Deployment is a snap
Cloud
• Microsoft, Amazon & Google now have
platforms for Machine Learning
• Ability to play with End-to-End tools for
free!
Major Players
Entering Market
• Ability to learn from the best in the
world
• Availability of open source data sets and
tools to experiment and learn
MOOC
Multiple factors are creating the perfect storm for ML
Democratization of
Predictive Analytics
• Market is ripe for
mainstream
adoption
• Companies with
access to data,
talent & the right
strategy are
poised to win
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But reality is that analytics is easier said than done
Source: MITSloan Management Review: Findings from 2016 Data & Analytics Global Executive Study and Research Project
Managing with
analytics is now a
mainstream idea,
though not a
mainstream
practice
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That trend is only increasing
…few companies have a strategic plan for analytics or are
executing a strategy for what they hope to achieve with analytics
Source: MITSloan Management Review: Findings from 2016 Data & Analytics Global Executive Study and Research Project
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Section 2
Machine Learning - Fundamentals
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ML falls within the Predictive Analytics step of the Analytics
Escalator
DESCRIPTIVE
• Reports
• Dashboard
• Business Intelligence
DIAGNOSTIC
• Queries
• Statistical Analysis
• Cubes/OLAP Tools
• Machine Learning
• Predictive models
• Forecasting
• Optimization
• Planning
PREDICTIVEPRESCRIPTIVE
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What is Machine Learning
“The goal of machine learning is to build computer systems
that can adapt and learn from their experience”
- Tom Dietterich
Thomas G. Dietterich is Emeritus
Professor of computer science at Oregon
State University. He is one of the
founders of the field of machine
learning.
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Example 1
Based on the size of the Tumor, predict whether the Tumor is Malignant or Benign
Tumor Size Result
3.4 Malignant
4.2 Benign
1.2 Benign
2.3 Benign
5.2 Malignant
4.7 Malignant
Tumor Size Result
1.9 ?
Classification Problem
Feature Label
Observations
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Example 2
Based on the size of the house, predict how much the house will sell for?
House Size Sales Price
2400 sq. ft. $230,000
3200 sq. ft. $410,000
1800 sq. ft. $167,000
2100 sq. ft. $225,000
3000 sq. ft. $350,000
2800 sq. ft. $310,000
House Size Sales Price
2200 sq. ft. ?
Regression Problem
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Section 3
Machine Learning – Framework & Process
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Process for solving real world problems
Ask the right
question
Frame the
question so that
Machine Learning
can be applied
Apply Machine
Learning
Reframe the
ML answer
to real-world
Extract Data
Develop
Model
Deploy Model
Evaluate
Model
Performance
Define Target
Metric
Extract Derived
Features
Select FeaturesFit Model
Evaluate
Models
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Machine Learning Framework
Input Data Function Output
Machine
Learning
Algorithm
y = f(x)
output
prediction
function
Feature -
House size
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Machine Learning Process
Training
Tumor Size Result
3.4 Malignant
4.2 Benign
1.2 Benign
2.3 Benign
5.2 Malignant
4.7 Malignant
Training Data Set
Training
Features
Training
Training
Label
Learned
Model
Testing
Tumor
Size
Result
1.9 ?
Testing
Features
Learned
Model Prediction
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What is NOT Machine Learning
Find the average sales price of the home
Calculating the # of Malignant vs Benign tumors
ML Goal: Build predictive models
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Section 4
Demo Time
Machine Learning in Action! Model Building, testing, deploying
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Source: Microsoft - Dig Deep with Azure Machine Learning
Here is how you select the Machine Learning Algorithm
