Overview on Machine Learning with solution from IBM. Presented to BESTT group in Thailand on 14 June 2017

1. Machine Learning and Cognitive
By Suwat Hongwiwat, Client Technology Architect, IBM Thailand
1

2. Structured Data
Unstructured Data
Analytics
2

3. Business leaders frequently make
decisions based on information they
don’t trust, or don’t have1in3
83%
of CIOs cited “Business
intelligence and analytics” as part
of their visionary plans
to enhance competitiveness
Business leaders say they don’t
have access to the information they
need to do their jobs
1in2
of CEOs need to do a better job
capturing and understanding
information rapidly in order to
make swift business decisions
60%
… and organizations
need deeper insights
Data is at the center
of a new wave of opportunity…
2.5 million items
per minute
300,000 tweets
per minute
200 million emails
per minute 220,000 photos
per minute
5 TB per flight
> 1 PB per day
gas turbines
1 ZB = 1 billion TB
3

4. Cloud Computing
Easy to Access Computing Power
Available of ready made “as-a-Services”
4

5. Artificial Intelligence
Cognitive Computing
Machine Learning
Deep Learning
Natural Language Processing
5

6. Definition
Machine Learning is a type of Artificial Intelligence that provides the ability to learn
without being explicitly programmed
Artificial Intelligence is the ability of a machine to think and act - Mimics the capability
of human brain in the areas of learning, problem solving etc.
Cognitive computing is the simulation of human thought processes in a computerized
model.
Cognitive computing systems use machine learning algorithms and embedded NLP.
Natural language processing (NLP) is the ability of a computer program to understand
human speech / text as it is spoken.
6

7. What is Machine Learning?
7

8. Machine Learning is computer that can learn to solve
problem without specific programming
8

9. Machine Learning have to works with a lot Data
9

10. Continuous learning is key for Machine Learning
Continuous
Monitor and Feedback
10

11. Step 1. Start with sample set of actual data with inputs and outputs
Price
1. Number of Room
2. Area – SQM
3. Location
Set of Data Bed room SQM Location Price
1 33 City 3,700,000
2 48 City Edge 4,000,000
3 75 Outside 5,000,000
Input Output
Sample Use case: Condo Seller
“คนเก็งกำไรซื ้อขำยคอนโด จะตัดสินใจซื ้อ หรือขำย จำกประสบกำรณ์ของ
เขำ ถ้ำเรำจะสอน machine ให้ซื ้อขำยคอนโด เรำจะทำได้อย่ำงไร?”
11

12. Input 2
Objective of algorithm is
 to find the value of weight
 that come out as nearest output value
 from set of sample data
Step 2. Feed Input data set to machine to calculate using “Algorithm”
to get Output
Output
Input 1
Input n
Machine
with
Algorithm
Field 1
Field 2
Field 3
Weight1
Weight2
Weight3
Output
Algorithm Calculation
Sample Use case: Condo Seller
Output
Output
12

13. Step 3. Compare calculated output with actual output and adjust
weight in algorithm, then go back to step 2 to get new closer output
Field 1
Field 2
Field 3
Weight1
Weight2
Weight3
Calculated
Output
Actual
OutputCompare
Sample Use case: Condo Seller
Stop iteration when get
minimum different value
between calculated and actual
13

14. There are a lot “Algorithm” being used in Machine Learning
and Deep Learning is the popular one
Regression Instance-based Regularization Decision Tree
NL Clustering Association Deep Learning
14

15. Machine Learning algorithm can be grouped as 2 models
• Supervised model – Learning by
examples , training , target
output
Eg: Dad explains his child about
different animals and its
characteristics (Sound it makes,
Apperance etc.)
Implemented for - Tickets problem
classification, Face recognition, Image
recognition etc.
• Unsupervised model – Learning by
experience, no training , no target
output
Eg: Visiting a new country without
knowing about their food, culture,
language etc. Learning by experience.
Implemented for – Text analytics,
Recommendations etc.
15

16. Machine Learning use cases will deal with large volume
of data
Use cases Explaination
Automated
loan
underwriting
Machine learning algorithms can be trained on millions of
examples of consumer data (age, job, marital status, etc)
and financial lending or insurance results (did this person
default, pay back the loan on time, get in a car accident,
etc). The underlying trends that can be assessed with
algorithms, and continuously analyzed to detect trends
that might influence lending and insuring into the future
Fraud
detection
Machine Learning can learn and monitor users’
behavioral patterns to identify anomalies and warning
signs of fraud attempts and occurrences, along with
collection of evidence necessary for conviction are also
becoming more commonplace in fighting crime.
16

17. People roles involve in Machine Learning
Data Engineering Data Scienctist Business Analysis App Development
Traditional Roles
DBA
Server
Admin
NW
Engineer
DC
Specialist
17

18. What IBM do?
18

19. IBM Machine Learning – Functionality for All!
IBM Watson Machine Learning
(on Bluemix)
Data Science Experience with
IBM Machine Learning
IBM Machine Learning for
z/OS (with DSX)
Data ScientistApp Developer Data Scientist
19

20. IBM Watson Machine Learning as-a-Services
20

21. IBM Data Science Experience
A L L Y O U R T O O L S I N O N E P L A C E
IBM Data Science Experience is an environment that brings together
everything that a Data Scientist needs. It includes the most popular
Open Source tools and IBM unique value-add functionalities with
community and social features, integrated as a first class citizen to
make Data Scientists more successful.
datascience.ibm.com
Powered by IBM Watson Data Platform
21

22. IBM Machine Learning platform – System z
22

23. IBM Machine Learning Platform - PowerAI
Enabled by High Performance Computing Infrastructure
Package of Pre-Compiled
Major Deep Learning
Frameworks
Easy to install & get started
with Deep Learning with
Enterprise-Class Support
Optimized for Performance
To Take Advantage of
NVLink
23

24. IBM Machine Learning Infrastructure
S822LC for HPC: recommended configuration for PowerAI
2 Socket, 4 GPU System with NVLink
Accelerated
Servers and
Infrastructure for
Scaling
Spectrum Scale:
High-Speed Parallel File
System
Scale to
Cloud
Cluster of NVLink Servers
24

25. Useful Link
What is Machine Learning ?
https://www.youtube.com/watch?v=WXHM_i-fgGo
Machine Learning Algorithms
https://www.youtube.com/watch?v=02R-lZYccEY
Natural language processing
https://www.youtube.com/watch?v=jubBtD-C9rw
https://www.youtube.com/watch?v=IKftaqRFyxE
Types of Learning
https://www.youtube.com/watch?v=gX4ORZ9geyc
Supervised Vs Unsupervised Model Learning
https://www.youtube.com/watch?v=nPFnlua2Y5Q
What is Cognitive ?
https://www.youtube.com/watch?v=h22n80aT2FY
How IBM Watson Works
https://www.youtube.com/watch?v=_Xcmh1LQB9I
25

26. Thank You
26