IBM Systems Magazine, Mainframe - July/August 2017 - 50

HOT TOPICS

To avoid this performance
degradation, monitor the
effectiveness of model predictions
by comparing them with actuals.
For instance, after some delay, a
bank will know which transactions
were fraudulent. Then it's possible
to compare the fraudulent
transactions with the anomalies
detected by the machine learning
model. From this comparison,
one can compute the accuracy
of the predictions. One can then
monitor this accuracy over time
and watch for drops. When a drop
happens, it's time to refresh the
machine learning model with
more up-to-date data, a feedback
loop (see Figure 1, below).
With a feedback loop, the
system learns continuously by
monitoring the effectiveness of
predictions and retraining when
needed. Monitoring and using
the resulting feedback are at the
core of machine learning. This is
no different from how humans
perform a new task. We learn from
our mistakes, adjust and act.

Business Use Strategy
The first pillar of the IBM Machine
Learning strategy is to support

users through this end-to-end
workflow by providing tools to
assist users in every step. A first
set of tools, IBM Data Science
Experience, deals with data
preparation, model creation and
model selection. A second set
of tools deals with models once
they are created. They include a
model repository with governance
support, a deployment interface
where one can create a
deployment in one click or one
API call, and a console to monitor
deployed models accuracy.
Machine learning is the union
of these.
The second pillar of the IBM
Machine Learning strategy is to
leverage open-source algorithms.
The open-source community is
providing state-of-the-art machine
learning technology for free. IBM
has started with Spark ML, a
leading-edge set of algorithms that
scale to petabyte data sets. IBM is
in the process of adding support
for popular packages for R, Python
and Java users. We are also in the
process of including support for
major deep learning packages.
The third pillar of the IBM
Machine Learning strategy is to

x

Machine learning
techniques
can provide
non-obvious
insight to
business
decisions

deliver it on every platform that
matters. This started with IBM
Machine Learning for z/OS* (ibm.
co/2pGrjVj). For other platforms,
IBM Watson* Machine Learning
is a public cloud version in beta
test, and a private-cloud offering
is in the works. One key feature
of this strategy is to allow cross
platform workflows; for instance,
have models created on the public
cloud platform, and then deploy
these models on a mainframe
for scoring. This hybrid cloud
strategy will be key to support the
user journey to hybrid cloud.
Machine learning techniques
can provide tremendous value
and non-obvious insight to
business decisions. As machine
learning adoption grows,
implementation characteristics
are becoming key factors
contributing to a successful
outcome. For example, the
currency of data that feeds the
algorithms can determine whether
the insight is usable in a real-time
context. Using most current
data for applications of machine
learning such as predicting client
loyalty and churn, determining
likelihood of default or fraud, or

Figure 1: Feedback Loop
Ingestion

History
Data

Data Prep

Train

Eval

Training &
Validation
Data

Test
Data

Deploy

Predict/
Score

Act

New
Data

Ingestion

Monitor

Feedback

50 // JULY/AUGUST 2017 ibmsystemsmag.com

pg 48-52.indd 4

6/13/17 10:31 AM


http://ibm.co/2pGrjVj http://ibm.co/2pGrjVj 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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