Splunk ITOA Roundtable - Zurich: 30th November 2017
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Presentation slides from the Splunk ITOA roundtable event that took place in Zurich, November 2017. Attendees learnt: - What is machine learning - Why machine learning is critical for today's IT - The challenges you will need to overcome - Some real examples of machine learning use cases - How to get started to machine learning
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Splunk ITOA Roundtable - Zurich: 30th November 2017
- 1. © 2017 SPLUNK INC.© 2017 SPLUNK INC. ITOA Roundtable 30.11.2017
- 2. © 2017 SPLUNK INC. ▶ 09:00 – Breakfast ▶ 09:30 – Niklaus Seiler to kick off ▶ 09:35 – Round table introductions. ▶ 10:00 – Presentation of ML and AIOps ▶ 10:30 – Break ▶ 10:45 – Round table discussion ▶ 12:00 – Lunch Agenda
- 3. © 2017 SPLUNK INC. Five things you will learn today What is Machine Learning Why ML is critical for today’s IT The challenges you will need to overcome Some real examples of ML use cases How to get started doing ML 1 2 3 4 5
- 4. © 2017 SPLUNK INC. Splunk turns machine data into answers Splunk’s trusted analytics platform empowers people to dive into their machine data so they can find answers quickly and see opportunities in real-time.
- 5. © 2017 SPLUNK INC. Machine Learning Deep Learning Artificial Intelligence Neural Networks Unsupervised Supervised What does it mean?
- 6. © 2017 SPLUNK INC. Machine learning used in our everyday lives Facial Recognition Product Recommendations Natural Language Processing Self Driving Cars
- 7. © 2017 SPLUNK INC. AI Machine Learning Deep Learning AI, Deep Learning, And Machine Learning Intelligent Agents No Human Involvement Sentient Machines Tensorflow Data sets are large and unknowable Guided Data Driven Decisions Augmenting Human Reasoning Operational Intelligence Splunk ML offerings today Neural Networks
- 8. © 2017 SPLUNK INC. Three Types Of Machine Learning Supervised Learning: Unsupervised Learning: Reinforcement Learning:
- 9. © 2017 SPLUNK INC. Deviation from past behavior Deviation from peers (aka Multivariate AD or Cohesive AD) Unusual change in features ITSI MAD Anomaly Detection Predict Service Outages Predicting Churn Predicting Events Trend Forecasting Detecting influencing entities Early warning of failure – predictive maintenance Identify peer groups Event Correlation Reduce alert noise ITSI Event Analytics Anomaly detection Predictive Analytics Clustering Typical ML use cases in Splunk
- 10. © 2017 SPLUNK INC. Custom Machine Learning – Success Formula Domain Expertise (IT, Security, …) Data Science Expertise Splunk Expertise Identify use cases Drive decisions Set business/ops priorities SPL Data prep Statistics/math background Algorithm selection Model building Splunk ML Toolkit facilitates and simplifies via examples & guidance Operational success
- 11. © 2017 SPLUNK INC. ▶ Assistants: Guided model building, testing and deployment for common objectives ▶ Showcases: Interactive examples for typical IT, security, business and IoT use cases ▶ Algorithms: 25+ standard algorithms included with the toolkit ▶ ML Commands: New SPL commands to fit, test and operationalize models ▶ Python for Scientific Computing Library: Access to 300+ open source algorithms Splunk Machine Learning Toolkit Extends Splunk platform functions and provides a guided modeling environment Build custom analytics for any use case
- 12. © 2017 SPLUNK INC. Demo
- 13. © 2017 SPLUNK INC. Machine Skills • Creative • Design • Empathy • Intuition • Sales • Marketing • Business Acumen • Analyzing high-velocity data • Analyzing big data sets • Complex data sets • Predicting future values • Detect Anomalies • Complex patterns Human Skills Human and machines
- 14. © 2017 SPLUNK INC. How long do you spend driving a car? How much of that time is productive? What’s the value?
- 15. © 2017 SPLUNK INC. How much time do we spend doing this in IT? Searching lots of data Creating static rules Monitoring Troubleshooting Root Cause Analysis Capacity Planning War Rooms Forecasting ….
- 16. © 2017 SPLUNK INC. Current Challenges that need to be addressed Lower customer satisfaction High cost of IT Operation Inefficient use of resources Lost revenue Most of IT budget is spent operating datacenters $426 Billion in downtime per year 300% increase in number of IT events over last 5 years Drop in customer satisfaction largely due to issues with digital channels
- 17. © 2017 SPLUNK INC. Analysing large sets of data Fast-flowing data Complex data Spotting complex patterns Reacting in real time And yet we said that machines are good at What is stopping us from doing ML for IT operations?
- 18. © 2017 SPLUNK INC. Collecting and analysing this data has never been so easy Network InfrastructureLayer Packet, Payload, Traffic, Utilization, Perf Storage Utilization, Capacity, Performance Server Performance, Usage, Dependency ApplicationLayer User Experience Usage, Response Time, Failed Interactions Byte Code Instrumentation Usage, Experience, Performance, Quality Business Performance Corporate Data, Intake, Output, Throughput Splunk Approach: ▶ Single repository for ALL data ▶ Data in original raw format ▶ Machine learning ▶ Simplified architecture ▶ Fewer resources to manage ▶ Collaborative approach MACHINE DATA
- 19. © 2017 SPLUNK INC. 1 2 3 4 5 6 How Splunk helps with Machine Learning Splunk Splunk ML Toolkit Third party Data Collection Data Cleansing & Preparation Analysis & Feature Extraction Create Models Refine Models and Algorithms Put into Production Splunk Apps with ML
- 20. © 2017 SPLUNK INC. AIOps Common technologies and data sources in use today
- 21. © 2017 SPLUNK INC.
- 22. © 2017 SPLUNK INC. Album: Honeymoon Common characteristic: Water Album: Honeymoon Common characteristic: Car Noise/Junk Album: Safari Common characteristic: Wildlife
- 23. © 2017 SPLUNK INC. Demo
- 24. © 2017 SPLUNK INC. What we just saw… Adaptive Thresholds Anomaly Detection Event Correlation Manage and maintain KPI thresholds by dynamically adapting to changing operational patterns Catch issues that thresholds can’t—baseline normal operations and alert on anomalous conditions Reduce event clutter, false positives and rules maintenance by auto-grouping related events
- 25. © 2017 SPLUNK INC. ▶ Modelling on ITSI Services and KPIs ▶ Predictive Analytics for future Service Health Scores ▶ Proactive alerting integrated into the ITSI notable events framework ▶ Identify leading indicators for possible service degradation Predict Service Health Score and Prevent Outages with Machine Learning https://www.splunk.com/blog/2017/08/28/itsi-and-sophisticated-machine-learning.html
- 26. © 2017 SPLUNK INC. Machine Learning on Events at the World Bank
- 27. Thanks to the integrated machine learning in Splunk ITSI, we now have a reduced number of events to process and the streamlined event analytics framework allows us to process events eight minutes more quickly Laurent Amouroux, Technical Director Econocom Infrastructure Management Services 15% increased SLA Performance 60% reduction In number of events 10x reduction in number of system performance events through machine learning
- 28. © 2017 SPLUNK INC. Outlier detected for faulty cell tower light The tower light turns on only at night, controlled by Canadian air traffic control
- 29. © 2017 SPLUNK INC. This aligns directly with a company’s top priorities Drive revenue 95% passengers through security >5 mins > spending more time shopping High inventory waste and food going stale > immediate sales insight Lower cost Hard to get threat insights > real-time security response Reduce risk Negative impact of outage at peak times > improved business & customer insight Customer experience
- 30. © 2017 SPLUNK INC. Where do I start? Better manage your alerts generated by your IT systems with ML Apply adaptive thresholds with ML to remove time configuring static rules Build a model to predict service outages Do not go big bang – start simple and prove value very quickly Faster detection of IT incidents IT teams more productive Prediction of failures
- 31. © 2017 SPLUNK INC. Open Discussion
- 32. © 2017 SPLUNK INC.© 2017 SPLUNK INC. Thank you.