IBM Systems Magazine, Mainframe - July/August 2017 - 28

Thinking Through
the Data
First, a quick definition: Machine
learning is a type of artificial
intelligence that enables a
computer to learn without
being explicitly programmed,
thus requiring less human
intervention. It uses what it
learns to anticipate or predict a
behavior or outcome.
"When data scientists are
solving a business problem, the
data within their organization
often holds the insights they need
to find a solution," says Terrie
Jacopi, IBM z Systems analytics
offering manager. "The broader
the set of data they can evaluate,
the better the outcomes. They
have to understand all of the
data sources that are relevant
to solving the problem and then
determine the best approach."
Again, the big challenge is
the amount of data out there.
Insights need to be gleaned not
only from an organization's
own information, but also from
outside sources, such as social
media and mobile devices. That
external data can originate from
current or potential customers.
For data scientists and their
organizations, channeling
that flood of data can be a

massive headache. It's also an
opportunity. "If you can take data
that's core to your business, like
transactional data that originates
on IBM z Systems, and combine
it with external data, the insights
you get about your clients are
phenomenal," Jacopi says.
To develop and exploit
those insights, data scientists
build behavioral models
based on patterns within data.
"Traditionally, data scientists do
a lot of manual, iterative model
development." Jacopi notes.
"Numerous algorithms might be
relevant, and they have to figure
out which approach would
be the best. Data scientists
spend a great deal of their time
identifying the best model by
trial and error, which takes
time away from addressing
the business challenge." And
time works against successful
model development. Models can
degrade as business, markets
and data change-which, as
most organizations know, can
happen rapidly.
These are the types of data
analysis puzzles IBM
Machine Learning
has been
designed to
help solve.

Model Making-and
Remaking
One of the capabilities offered
by IBM Machine Learning for
z/OS is the Cognitive Assistant
for Data Scientists. This IBMpatented technology helps
eliminate the trial-and-error
process so data scientists can
consistently and more easily
choose the best model to address
business problems.
A second key IBM Machine
Learning capability is Hyper
Parameter Optimization (HPO).
"At times, a data scientist can
have as many as 100 parameters
to consider," Jacopi says. "This
makes it time-consuming and
difficult to determine which are
the most critical parameters to
optimize the model and gain
the best outcome." HPO aids
the data scientist in selecting
the best parameters to optimize
the predictive capabilities
of the model.
Once the model has been
tested to ascertain that it's
providing useful, accurate
predictions, putting that model
to work "is a push-button
deployment," Jacopi says.
Clients have told her it
can take anywhere
from three to six

Learn More

Bring machine learning
to where your data resides.
See what products are available,
get tools for data scientists,
find resources and more:
ibm.com/
machine-learning

28 // JULY/AUGUST 2017 ibmsystemsmag.com

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6/13/17 10:11 AM


http://www.ibm.com/machine-learning http://www.ibm.com/machine-learning http://www.ibmsystemsmag.com

Table of Contents for the Digital Edition of IBM Systems Magazine, Mainframe - July/August 2017

Table of Contents
Editor's Desk: Learning About Cognitive Capabilities
Partner PoV: A Clear Picture: IT operational analytics tools can provide a view of management data
Trends: The Connected Mainframe: Java on z Systems delivers portability, security and other benefits
Currents: Keeping Out Risks: Secure Service Containers are a virtual appliance framework for sensitive workloads
IT Today: Protecting Your Crown Jewels: IBM Guardium updates help organizations reduce risk and demonstrate compliance
Feature: Accounting for the Future: Bankwest modernizes its mainframe integration environment with a RESTful API framework and Java
Cover Story: Modeling Machine Learning: Cognitive on IBM z gives clients the tools to make better, faster decisions
Feature: A Thoughtful Pairing: Keeping IBM Machine Learning z/OS next to the data on the mainframe enhances analytics
HotTECH Products
TECH Showcase: Plan for Protection: Five points to consider when preparing for backup and recovery
Hot Topics: Optimized for the Enterprise: IBM Machine Learning for z/OS is supported by Acache Spark
Solutions: JES2MAIL/JES2FTP V5.1, CASI Software; ReACT V5.0, Advanced Software Product Group Inc.; FDR/UPSTREAM Web Portal V2.0.00, INNOVATION Data Processing
Stop Run: Traveling Through Time: IBM archivist assists 'Hidden Figures' movie production with authentic artifacts
Reference Point - Global Events, Education, Resources for Mainframe
2017 Mainframe Solutions Edition
IBM Systems Magazine, Mainframe - July/August 2017 - Intro
IBM Systems Magazine, Mainframe - July/August 2017 - Cover1
IBM Systems Magazine, Mainframe - July/August 2017 - Cover2
IBM Systems Magazine, Mainframe - July/August 2017 - 1
IBM Systems Magazine, Mainframe - July/August 2017 - Table of Contents
IBM Systems Magazine, Mainframe - July/August 2017 - 3
IBM Systems Magazine, Mainframe - July/August 2017 - 4
IBM Systems Magazine, Mainframe - July/August 2017 - 5
IBM Systems Magazine, Mainframe - July/August 2017 - Editor's Desk: Learning About Cognitive Capabilities
IBM Systems Magazine, Mainframe - July/August 2017 - 7
IBM Systems Magazine, Mainframe - July/August 2017 - Partner PoV: A Clear Picture: IT operational analytics tools can provide a view of management data
IBM Systems Magazine, Mainframe - July/August 2017 - 9
IBM Systems Magazine, Mainframe - July/August 2017 - Trends: The Connected Mainframe: Java on z Systems delivers portability, security and other benefits
IBM Systems Magazine, Mainframe - July/August 2017 - 11
IBM Systems Magazine, Mainframe - July/August 2017 - 12
IBM Systems Magazine, Mainframe - July/August 2017 - Currents: Keeping Out Risks: Secure Service Containers are a virtual appliance framework for sensitive workloads
IBM Systems Magazine, Mainframe - July/August 2017 - 14
IBM Systems Magazine, Mainframe - July/August 2017 - 15
IBM Systems Magazine, Mainframe - July/August 2017 - IT Today: Protecting Your Crown Jewels: IBM Guardium updates help organizations reduce risk and demonstrate compliance
IBM Systems Magazine, Mainframe - July/August 2017 - 17
IBM Systems Magazine, Mainframe - July/August 2017 - 18
IBM Systems Magazine, Mainframe - July/August 2017 - 19
IBM Systems Magazine, Mainframe - July/August 2017 - Feature: Accounting for the Future: Bankwest modernizes its mainframe integration environment with a RESTful API framework and Java
IBM Systems Magazine, Mainframe - July/August 2017 - 21
IBM Systems Magazine, Mainframe - July/August 2017 - 22
IBM Systems Magazine, Mainframe - July/August 2017 - 23
IBM Systems Magazine, Mainframe - July/August 2017 - 24
IBM Systems Magazine, Mainframe - July/August 2017 - 25
IBM Systems Magazine, Mainframe - July/August 2017 - Cover Story: Modeling Machine Learning: Cognitive on IBM z gives clients the tools to make better, faster decisions
IBM Systems Magazine, Mainframe - July/August 2017 - 27
IBM Systems Magazine, Mainframe - July/August 2017 - 28
IBM Systems Magazine, Mainframe - July/August 2017 - 29
IBM Systems Magazine, Mainframe - July/August 2017 - Feature: A Thoughtful Pairing: Keeping IBM Machine Learning z/OS next to the data on the mainframe enhances analytics
IBM Systems Magazine, Mainframe - July/August 2017 - 31
IBM Systems Magazine, Mainframe - July/August 2017 - 32
IBM Systems Magazine, Mainframe - July/August 2017 - 33
IBM Systems Magazine, Mainframe - July/August 2017 - 34
IBM Systems Magazine, Mainframe - July/August 2017 - 35
IBM Systems Magazine, Mainframe - July/August 2017 - HotTECH Products
IBM Systems Magazine, Mainframe - July/August 2017 - 37
IBM Systems Magazine, Mainframe - July/August 2017 - 38
IBM Systems Magazine, Mainframe - July/August 2017 - 39
IBM Systems Magazine, Mainframe - July/August 2017 - 40
IBM Systems Magazine, Mainframe - July/August 2017 - TECH Showcase: Plan for Protection: Five points to consider when preparing for backup and recovery
IBM Systems Magazine, Mainframe - July/August 2017 - 42
IBM Systems Magazine, Mainframe - July/August 2017 - 43
IBM Systems Magazine, Mainframe - July/August 2017 - 44
IBM Systems Magazine, Mainframe - July/August 2017 - 45
IBM Systems Magazine, Mainframe - July/August 2017 - 46
IBM Systems Magazine, Mainframe - July/August 2017 - 47
IBM Systems Magazine, Mainframe - July/August 2017 - Hot Topics: Optimized for the Enterprise: IBM Machine Learning for z/OS is supported by Acache Spark
IBM Systems Magazine, Mainframe - July/August 2017 - 49
IBM Systems Magazine, Mainframe - July/August 2017 - 50
IBM Systems Magazine, Mainframe - July/August 2017 - 51
IBM Systems Magazine, Mainframe - July/August 2017 - 52
IBM Systems Magazine, Mainframe - July/August 2017 - 53
IBM Systems Magazine, Mainframe - July/August 2017 - Solutions: JES2MAIL/JES2FTP V5.1, CASI Software; ReACT V5.0, Advanced Software Product Group Inc.; FDR/UPSTREAM Web Portal V2.0.00, INNOVATION Data Processing
IBM Systems Magazine, Mainframe - July/August 2017 - 55
IBM Systems Magazine, Mainframe - July/August 2017 - Stop Run: Traveling Through Time: IBM archivist assists 'Hidden Figures' movie production with authentic artifacts
IBM Systems Magazine, Mainframe - July/August 2017 - Cover3
IBM Systems Magazine, Mainframe - July/August 2017 - Cover4
IBM Systems Magazine, Mainframe - July/August 2017 - Reference Point - Global Events, Education, Resources for Mainframe
IBM Systems Magazine, Mainframe - July/August 2017 - 2017 Mainframe Solutions Edition
IBM Systems Magazine, Mainframe - July/August 2017 - SE2
IBM Systems Magazine, Mainframe - July/August 2017 - SE3
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