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Machine Learning & IT Service Intelligence
for the Enterprise
May 2017
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
Speakers
3Syncsort Confidential and Proprietary - do not copy or distribute
Zhe “Maggie” Li
Chief Architect
Speakers
4Syncsort Confidential and Proprietary - do not copy or distribute
Ian Hartley
Principal Engineer
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
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
What is IT Service Intelligence for the Enterprise?
7Syncsort Confidential and Proprietary - do not copy or distribute
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…”
Is Machine Learning Being Tried Today?
10Syncsort Confidential and Proprietary - do not copy or distribute
Is Machine Learning Being Tried Today?
Is Machine Learning Being Tried Today?
12Syncsort Confidential and Proprietary - do not copy or distribute
Is Machine Learning Working Well Today?
Is Machine Learning the [Near] Future?
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)
Machine Learning for the Enterprise - No Longer a “Future?”
Syncsort Confidential and Proprietary - do not copy or distribute 15
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.
Categories of Techniques
Supervised Learning
Unsupervised Learning
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
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
Machine Data-driven Analytics
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
Critical Machine Data  Streamed to a Big Data Platform (e.g. Splunk)
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
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
25Syncsort Confidential and Proprietary - do not copy or distribute
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
Splunk Platform Machine Learning Visualizations
28
29
30
31
32
Syncsort’s MFX Performance Analyzer
33
Syncsort MFX Performance Presented in Hierarchical Format
34
MFX Performance by LPAR
35
MFX Performance by Time Slice
36
IT Service Intelligence and Machine Learning – Buying Yourself Time!
37Syncsort Confidential and Proprietary - do not copy or distribute
© 2017 SPLUNK INC.© 2017 SPLUNK INC.
User Needs
38
When creating KPIs I don’t know what my threshold levels
should be
I know my data behaves differently during different times of the week
My data is not static
© 2017 SPLUNK INC.© 2017 SPLUNK INC.
Real Data Varies By Time
39
Increase at 4.30am and die down at 5.30pm
A single threshold policy will not be effective
Need the ability to have different thresholds at different times in
the day
Requests per second
© 2017 SPLUNK INC.© 2017 SPLUNK INC.
Anomaly Detection
© 2017 SPLUNK INC.© 2017 SPLUNK INC.
Anomaly Example
41
7 Days
7 days of training data with no drastic anomalies
8th day, the obvious anomaly will be detected
Mini spikes are not necessarily anomalies
8th day
© 2017 SPLUNK INC.© 2017 SPLUNK INC.
Anomaly Example
42
7 Days
7 days of training data with one anomaly
9th day, the data point is not as severe an anomaly anymore
If this data-point occurs at another point in time, it will be an anomaly
9th day
© 2017 SPLUNK INC.© 2017 SPLUNK INC.
Anomaly Example
43
Sudden dip in requests
Policies with static thresholds may not catch issue
Policies with adaptive thresholds may not catch issue due to larger granularity of
time
Anomaly detection will catch such a deviation
© 2017 SPLUNK INC.© 2017 SPLUNK INC.
Anomaly Detection Screenshot
© 2017 SPLUNK INC.© 2017 SPLUNK INC.
Event Analytics:
Smart Mode
© 2017 SPLUNK INC.© 2017 SPLUNK INC.
What is it doing?
© 2017 SPLUNK INC.© 2017 SPLUNK INC.
Smart Mode Screenshots
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
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

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Machine Learning & IT Service Intelligence for the Enterprise: The Future is Already Here!

  • 1. Machine Learning & IT Service Intelligence for the Enterprise May 2017
  • 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
  • 3. Speakers 3Syncsort Confidential and Proprietary - do not copy or distribute Zhe “Maggie” Li Chief Architect
  • 4. Speakers 4Syncsort Confidential and Proprietary - do not copy or distribute Ian Hartley Principal Engineer
  • 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…”
  • 9. Is Machine Learning Being Tried Today?
  • 10. 10Syncsort Confidential and Proprietary - do not copy or distribute Is Machine Learning Being Tried Today?
  • 11. Is Machine Learning Being Tried Today?
  • 12. 12Syncsort Confidential and Proprietary - do not copy or distribute Is Machine Learning Working Well Today?
  • 13. Is Machine Learning the [Near] Future?
  • 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.
  • 17. Categories of Techniques Supervised Learning Unsupervised Learning
  • 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
  • 22. Critical Machine Data  Streamed to a Big Data Platform (e.g. Splunk)
  • 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
  • 25. 25Syncsort Confidential and Proprietary - do not copy or distribute
  • 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
  • 27. Splunk Platform Machine Learning Visualizations
  • 28. 28
  • 29. 29
  • 30. 30
  • 31. 31
  • 32. 32
  • 34. Syncsort MFX Performance Presented in Hierarchical Format 34
  • 36. MFX Performance by Time Slice 36
  • 37. IT Service Intelligence and Machine Learning – Buying Yourself Time! 37Syncsort Confidential and Proprietary - do not copy or distribute
  • 38. © 2017 SPLUNK INC.© 2017 SPLUNK INC. User Needs 38 When creating KPIs I don’t know what my threshold levels should be I know my data behaves differently during different times of the week My data is not static
  • 39. © 2017 SPLUNK INC.© 2017 SPLUNK INC. Real Data Varies By Time 39 Increase at 4.30am and die down at 5.30pm A single threshold policy will not be effective Need the ability to have different thresholds at different times in the day Requests per second
  • 40. © 2017 SPLUNK INC.© 2017 SPLUNK INC. Anomaly Detection
  • 41. © 2017 SPLUNK INC.© 2017 SPLUNK INC. Anomaly Example 41 7 Days 7 days of training data with no drastic anomalies 8th day, the obvious anomaly will be detected Mini spikes are not necessarily anomalies 8th day
  • 42. © 2017 SPLUNK INC.© 2017 SPLUNK INC. Anomaly Example 42 7 Days 7 days of training data with one anomaly 9th day, the data point is not as severe an anomaly anymore If this data-point occurs at another point in time, it will be an anomaly 9th day
  • 43. © 2017 SPLUNK INC.© 2017 SPLUNK INC. Anomaly Example 43 Sudden dip in requests Policies with static thresholds may not catch issue Policies with adaptive thresholds may not catch issue due to larger granularity of time Anomaly detection will catch such a deviation
  • 44. © 2017 SPLUNK INC.© 2017 SPLUNK INC. Anomaly Detection Screenshot
  • 45. © 2017 SPLUNK INC.© 2017 SPLUNK INC. Event Analytics: Smart Mode
  • 46. © 2017 SPLUNK INC.© 2017 SPLUNK INC. What is it doing?
  • 47. © 2017 SPLUNK INC.© 2017 SPLUNK INC. Smart Mode Screenshots
  • 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