© 2017 IBM Corporation

Disruptive TechnologiesData Science & AI-Machine Learning Alvin C. Francis

Program Director,

Predictive Analytics & Manager IBM Machine Learning Hub Canada

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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

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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

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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.

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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?

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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

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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 YOUalvin.francis1@ibm.com

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• © 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.