Today’s enterprises with mainframes and Cloud/server architectures are facing new issues and challenges, among the top of which are security and automation of operations. As the sheer amount of data housed on mainframes rises, daily operations have become more complex and more difficult to handle manually. Whether you’re a CIO, CISO, VP of Infrastructure and/or Operations, or an all-important IT practitioner, you need new ways to approach and address these challenges as well as the opportunities that come with driving this type of change. In this webcast, you’ll learn: • What is Machine Learning: The Vision vs. Reality • The Challenges Driving Automated Mainframe Operations • Use Cases for Machine Learning at Mainframe Enterprises

1. Machine Learning for Your Enterprise:
Operations and Security for Mainframe Enterprises

2. Housekeeping
Webcast Audio:
– Today’s webcast audio is streamed through your computer speakers.
– If you need technical assistance with the web interface or audio, please
reach out to us using the chat window.
Questions Welcome:
– Submit your questions at any time during the presentation using the
chat window.
– We will answer them during our Q&A session following the
presentations.
Recording and Slides:
– This webcast is being recorded. You will receive an email following the
webcast with a link to download both the recording and the slides.
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3. Session Abstract and Speakers
Machine Learning for Your Enterprise: Operations and Security for Mainframe
Enterprises
– What is Machine Learning: The Vision vs. Reality
– The Challenges Driving Automated Mainframe Operations
– Use Cases for Machine Learning at Mainframe Enterprises
The presenters will also do an open Q&A with you and discuss results from our interactive quick-
polls conducted during the session.
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Zhe “Maggie” Li
Chief Architect
Steven Menges, Director,
Product Management
David Hodgson,
General Manager/CPO

4. Speakers
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Zhe “Maggie” Li
Chief Architect

5. Speakers
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David Hodgson,
General Manager/CPO

6. Machine Learning Poll #1
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Q1.Which Big Data analytics platforms does your company use today?
o Hadoop
o Splunk
o Elastic / ELK stack
o SAS
o Other Data Warehouse
o Don’t Know
(Check all that apply)

7. 77Syncsort Confidential and Proprietary - do not copy or distribute
Enterprise Computing – Mainframe?

8. 88Syncsort Confidential and Proprietary - do not copy or distribute
2000+ Organizations Overall
71%
Fortune 500
2.5 BillionBus. Transactions / day / per MF
23of Top 25
US Retailers
of World’s
Top Insurers10Top World
Banks92
Source: IBM
Mainframe in Enterprises Today

9. Enterprises With Mainframes Facing New Challenges
Security
– Mainframes are connected to mobile, IOT, cloud and open systems
– External attacks
– Internal threats (unknown unknown)
Automation of IT Operations
– Transactions grow exponentially
– Increased complexity
– Aging problem for mainframe skilled population
– Lower costs required

10. Machine Learning for the Enterprise - No Longer a “Future?”
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11. What is Machine Learning?
“Machine Learning is a fascinating field of artificial intelligence research and
practice where we investigate how computer agents can improve their
perception, cognition, and action with experience. Machine Learning is about
machines improving from data, knowledge, experience, and interaction…”

12. Machine Learning
Machine learning uses algorithms to build analytical
models and help computers “learn” from data.
It makes predictions and uncovers hidden insights about
relationships and trends.

13. Machine Learning

14. Categories of Techniques
Supervised Learning
Unsupervised Learning

15. Categories of Techniques
Supervised Learning: Have the idea that there is a relationship between the
input and the output.
• Regression model: predict continuous valued output
• Housing price
• Weather forecast
• Classification model: map input variables into discrete categories.
• Identify cancer
• Handwriting detection
Unsupervised Learning: little or no idea what our results should look like.
• Clustering:
• Market segmentation
• Social network analysis
• Anomaly detection

16. Predict with Machine Learning
actual data input
y = H (X) prediction = H (X)
hypothesis new input

17. The Vision vs. Reality

18. Machine Data-driven Analytics

19. Machine Learning Poll #2
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Q2. Is Mainframe SMF and/or “log” data going into your big data
platform/repository?
o Yes, it is being streamed into it today
o Yes, it goes into it via periodic batch/other input method
o No, but that data has been requested/is desired
o No
o Don’t Know

20. Reminder
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Type in your questions at any time during the
presentation using the chat window.
We will answer them during our Q&A session following
the presentations or afterward.

21. Examples
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22. Critical Machine Data  Streamed to a Big Data Platform

23. Critical Mainframe Machine Data 
Normalized and Streamed to Splunk with Ironstream®
Log4jFile
Load
SYSLOG
SYSLOGD
logs
security
SMF
50+
types
RMF
Up to 50,000
values
DB2SYSOUT
Live/Stored
SPOOL Data
Alerts
Network
Components
Ironstream
API
Application Data
Assembler
C
COBOL
REXX
USS

24. Machine Data  Machine Learning Platform - High Level Architecture
Send TCP
Send HTTP
Send Kafka
Predictive Analytics
With Machine Learning
Splunk/
Hadoop/
Cloud
Get TCP
Get HTTP
Consume Kafka
Automation tools
Other Apps
Operator
commands
Dynamic
reconfiguration
Data collection
Data Transformation
Data lineage/Metering
data
feedback
z/OS
Ironstream
Configuration
GUI

25. Splunk Platform Machine Learning Toolkit
The Machine Learning Toolkit App delivers new SPL commands,
custom visualizations, assistants, and examples to explore a variety
of ml concepts.
Assistants:
– Predict Numeric Fields (Linear Regression): e.g. predict median house
values.
– Predict Categorical Fields (Logistic Regression): e.g. predict customer
churn.
– Detect Numeric Outliers (distribution statistics): e.g. detect outliers in IT
Ops data.
– Detect Categorical Outliers (probabilistic measures): e.g. detect outliers
in diabetes patient records.
– Forecast Time Series: e.g. forecast data center growth and capacity
planning.
– Cluster Numeric Events: e.g. Cluster Hard Drives by SMART Metrics

26. The Basic Process of Machine Learning
Clean and transform your data
– To meet the analytics explicit requirements
Fit the model
– Toolkit features 27 algorithms for fitting models
– Over 300 open source Python algorithms in the add-on
Validate the model
– Each assistant provides a few methods in the validate section
Refine the model
– Adjust the parameters to improve the metrics
Deploy the model
– Deployment actions fall into the following categories
• Make prediction or forecast
• Detect outliers and anomalies
• Trigger or inform an action

27. Splunk Platform Machine Learning Visualizations

28. 2828
Use Case Areas
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• RACF/ACF2/TSS
Authentications
• TSO account & login
activity
• FTP sessions & file
activity
• Sensitive data access
& movement
(PII/PHI)
• Configuration
settings (e.g. FISMA)
• IRS Pub 1075
• Incident triage
• Response
times/SLAs
• Latencies
• Exceptions
• Resource utilization
• Anomalous behavior
detection
• Glass table view of entire
service
• Predictive analytics
Security
Trouble-
Shooting
Health
Monitoring
Compliance

29. Summary
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30. Questions and More Information
Additional Questions for David and Maggie?
For More Information:
syncsort.com/ironstream
blog.syncsort.com/
Try Ironstream for Free:
syncsort.com/ironstreamstarteredition
Comments/Other:
Steven Menges: smenges@syncsort.com
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