Enterprises with mainframes and Cloud/server architectures face unique issues and challenges and if your enterprise delivers a service whose operation spans mainframe and distributed and/or Cloud infrastructures (e.g. a mobile banking/customer app), this webinar is for you.
See how you can gain unique business and service-relevant context using your own machine data, including that from your z/OS mainframe. Implicitly learn patterns, eliminate costly false alerts, identify anomalies, and baseline normal operations by employing advanced analytics driven by machine learning. You’ll also see and learn about:
• Accelerating root-cause analysis and getting ahead of customer-impacting outages and slow-downs for your service
• “Glass Table” view for clickable visualization of the entire service-relevant infrastructure
• Machine Learning in IT Service Intelligence
• The Machine Learning Toolkit available today
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.
2
5. Session Abstract and Speakers/Guests
See how you can gain unique business and service-relevant context using your own machine data, including that from your z/OS
mainframe. Implicitly learn patterns, eliminate costly false alerts, identify anomalies, and baseline normal operations by
employing advanced analytics driven by machine learning. We’ll discuss:
Accelerating root-cause analysis and getting ahead of customer-impacting outages and slow-downs for your service
“Glass Table” view for clickable visualization of the entire service-relevant infrastructure
Machine Learning in IT Service Intelligence
The Machine Learning Toolkit available today
5Syncsort Confidential and Proprietary - do not copy or distribute
Zhe “Maggie” Li
Chief Architect
Ian Hartley
Principal Engineer
Alok Bhide
Director, Product Mgt
6. What is an “Enterprise” ?
6Syncsort 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 Banks
92
Source: IBM
7. What is IT Service Intelligence for the Enterprise?
7Syncsort Confidential and Proprietary - do not copy or distribute
8. What is Machine Learning for the Enterprise?
“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…”
14. Poll #1
Syncsort Confidential and Proprietary - do not copy or distribute 14
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)
15. Machine Learning for the Enterprise - No Longer a “Future?”
Syncsort Confidential and Proprietary - do not copy or distribute 15
16. 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.
18. 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
19. 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
21. Poll #2
Syncsort Confidential and Proprietary - do not copy or distribute 21
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
23. Syncsort Ironstream®- Splunk: High-level Architecture
23Syncsort Confidential and Proprietary - do not copy or distribute
Mainframe
TCP/IP
(SSL)
Data Forwarder DCE IDT
Ironstream DesktopData Collection Extension
Data ForwarderData Forwarder
DB2SYSOUT
Live/Stored
SPOOL Data
Alerts
Network
Components
Ironstream API
Application Data
Assembler
C
COBOL
REXX
USSLog4jFile
Load
z/OS
SYSLOG
SYSLOGD
logs
security
SMF
50+
types
RMF
Up to 50,000
values
Enterprise Security
ACK
24. 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
48. Questions and More Information
Questions for the Panel?
For More Information:
http://www.syncsort.com/ITSI
www.Splunk.com/ITSI
www.splunk.com/en_us/resources/machine-learning.html
Try Ironstream for Free:
syncsort.com/ironstreamstarteredition
Comments/Other: info@syncsort.com
48Syncsort Confidential and Proprietary - do not copy or distribute
49. Syncsort Ironstream®- Splunk: High-level Architecture
49Syncsort Confidential and Proprietary - do not copy or distribute
Mainframe
TCP/IP
(SSL)
Data Forwarder DCE IDT
Ironstream DesktopData Collection Extension
Data ForwarderData Forwarder
DB2SYSOUT
Live/Stored
SPOOL Data
Alerts
Network
Components
Ironstream API
Application Data
Assembler
C
COBOL
REXX
USSLog4jFile
Load
z/OS
SYSLOG
SYSLOGD
logs
security
SMF
50+
types
RMF
Up to 50,000
values
Enterprise Security
ACK