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© 2017 IBM Corporation
Disruptive TechnologiesData Science & AI-Machine Learning Alvin C. Francis
Program Director,
Predictive Analytics & Manager IBM Machine Learning Hub Canada
IBM Analytics
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About me
1. Program Director, Analytics Business Unit
- Statistical Analysis
-Predictive Analytics
-Algorithms & Machine Learning components • Consumed by Statistical & Predictive solutions and in Watson Machine Learning
2. Manager IBM Machine Learning Hub Canada-Place to collaborate with clients on Machine Learning
2Machine Learning & Data Science
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are vulnerableto disruption within
three years 72%
Digital businesses are disrupting industries and professions
Machine Learning & Data Science
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Disruption is driven and enabled by IT
Machine Learning & Data Science
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Every Industry is Affected
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Banking Financial Services Retail
Health Care Manufacturing Telecommunications
Machine Learning & Data Science
$10s of Billions$$$$$
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Telecom Industry“61% of CSPs point to Google as posing the biggest competitive threat to their business”
Heavy Reading: Webscale Internet Companies: New Drivers of the Network Equipment Market
“The global telecoms industry landscape is changing faster than ever. Erosion of legacy revenue streams driven by over-the-top (OTT) competitors continues, forcing operators to consider new ways of remaining relevant to consumer and enterprise customers.”
EY:Digital transformation for 2020 and beyond - A global telecommunications study
Machine Learning & Data Science
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AI & Data Science are key agents of disruption in Telecoms
31%
69%
31% are leveraging existing investments & infrastructure
IDC has predicted that within Telco organizations:
69% are making new technology investments for AI systems
Machine Learning & Data Science
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Data Science is
the practice of various scientific fields,
their algorithms, approaches and processes,
through the use of programming languages and software frameworks,
that aims to extract knowledge, insights and recommendations from data,
and deliver them to business users and consumers in consumable applications.
Machine Learning & Data Science
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Artificial Intelligence vs. Machine Learning
Machine Learning is an application of AI where we give machines access to historical data and let them learn for themselves.
Artificial Intelligence: Machines being able to carry out tasks in a way that we would consider “smart”- Copying Intelligent Human Behavior
Machine Learning & Data Science
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Hyper or Radical Personalization
Machine Learning & Data Science
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Price and Product Optimization
Machine Learning & Data Science
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Predictions and Classifications
Machine Learning & Data Science
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Discover Patterns, Anomalies, and Trends
Machine Learning & Data Science
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and to beat humans … Jeopardy in 2011
Machine Learning & Data Science
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Telecom Predictive Maintenance
wear
fatigue
thermal stress
physical damage
material buildup
corrosion
usage
abuse
time
master data
Fix problems with telecom hardware (such as cell towers, power lines, Power Generators etc.) before they happen, by detecting signals that usually lead to failure
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Clearly Articulate Use Case
Gather all the Data
Apply
Machine Learning
Prepare Data
DigitalApplication
Evaluate
Steps to put Data Science to work..
Machine Learning & Data Science
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Data Predictions & Insight
“Computers that learn without being explicitly programmed”“Using algorithms to understand patterns in data”
Algorithms
Machine Learning… What is it?
Machine Learning & Data Science
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Machine Learning 101
Find Patterns
Train Algorithm Recognizes Patterns
DataContains Patterns
Historical Data
Identify Patternsnot recognizable by
humans 1Build Model
Use Model
Build Modelsfrom those patterns2
Data
New Data
Make PredictionsWith the deployed models3
Machine Learning & Data Science
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Supervised Learning
xx
x xx
xx
xx
X 1
X 2
boundary
All data is labeled and the algorithms learn to predict the output from the input data.
Machine Learning & Data Science
Fraud detection (fraud, not fraud)
Text sentiment analysis (happy, not happy )
Network Security - ( attack, not attack)
Image Identification - What type of animal is this ?
Customer segmentation for targeted marketing
All data is unlabeled and the algorithms learn to inherent structure from the input data
clusters
X 1
X 2
Unsupervised Learning
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IBM Data Science Experience
IBM Embraces Open Source for Data Science
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USA⎮San Jose
GERMANY⎮Boeblingen
CANADA Markham ⎮
INDIA⎮Bangalore
CHINA Beijing⎮
5 Locations - One mission ! Collaborate with clients, Share best practices.
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3 collaboration Tracks New to Machine Learning?
Learn about ML hottest Industry trends
Take an ML 101 course with hands on exercises
Practice on uses cases that are applicable to your industry
Bring your own data or use publicly available
Have an ML challenge that you would like to collaborate on ?
Work with IBM Data scientists for 2 days
Bring your own sample data to the ML Hub
−Analyze &prepare data
− Feature Engineering
−Create, Evaluate & optimize models
Want to Learn about IBM latest innovations in ML?
Data Science Experience
Continuous Feedback & Retraining
Driving efficiency & accuracy via Automation
Machine Learning & Data Science
What role will/should Data Science & AI play in new networking technologies such as 5G, IOT, NFV, SDN ?
How will Network manufacturers, CSPs and Application developers use Data Science and AI to differentiate their offerings?
“Machine Learning is to the 21st Century, what the Industrial Revolution was to the 18th Century” Rob Thomas, GM IBM Analytics
ETSI: Experiential Networked Intelligence Specification Group Goal: Improve operators' experience regarding network deployment and operation, by using AI techniques.
© 2017 IBM Corporation
THANK [email protected]
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Legal Disclaimer
• © IBM Corporation 2017. All Rights Reserved.• The information contained in this publication is provided for informational purposes only. While efforts were made to verify the completeness and
accuracy of the information contained in this publication, it is provided AS IS without warranty of any kind, express or implied. In addition, this information is based on IBM’s current product plans and strategy, which are subject to change by IBM without notice. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, this publication or any other materials. Nothing contained in this publication is intended to, nor shall have the effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the applicable license agreement governing the use of IBM software.
• References in this presentation to IBM products, programs, or services do not imply that they will be available in all countries in which IBM operates. Product release dates and/or capabilities referenced in this presentation may change at any time at IBM’s sole discretion based on market opportunities or other factors, and are not intended to be a commitment to future product or feature availability in any way. Nothing contained in these materials is intended to, nor shall have the effect of, stating or implying that any activities undertaken by you will result in any specific sales, revenue growth or other results.