1. Copyright © 2016 Splunk Inc.
Operationalizing Machine Learning
Adrish Sannyasi
Staff Solutions Architect, Healthcare
Splunk, Inc.
Dr. Tom LaGatta
Staff Data Scientist
Splunk, Inc.

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Disclaimer
During the course of this presentation, we may make forward looking statements regarding future
events or the expected performance of the company. We caution you that such statements reflect our
current expectations and estimates based on factors currently known to us and that actual events or
results could differ materially. For important factors that may cause actual results to differ from those
contained in our forward-looking statements, please review our filings with the SEC. The forward-looking
statements made in the this presentation are being made as of the time and date of its live presentation.
If reviewed after its live presentation, this presentation may not contain current or accurate information.
We do not assume any obligation to update any forward looking statements we may make.
In addition, any information about our roadmap outlines our general product direction and is subject to
change at any time without notice. It is for informational purposes only and shall not, be incorporated
into any contract or other commitment. Splunk undertakes no obligation either to develop the features
or functionality described or to include any such feature or functionality in a future release.

3. Copyright © 2016 Splunk Inc.
Why do we need ML?

4. Copyright © 2016 Splunk Inc.
Historical Data Real-time Data Statistical Models
DB, Hadoop/S3/NoSQL, Splunk Machine Learning
T – a few days T + a few days
Why is this so challenging using traditional methods?
• DATA IS STILL IN MOTION, still in a BUSINESS PROCESS.
• Enrich real-time MACHINE DATA with structured HISTORICAL DATA
• Make decisions IN REAL TIME using ALL THE DATA
• Combine LEADING and LAGGING INDICATORS (KPIs)
Splunk
Security Operations Center
Network Operations Center
Business Operations Center

5. Copyright © 2016 Splunk Inc.
What is ML?

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ML 101: What is it?
• Machine Learning (ML) is a process for generalizing from examples
– Examples = example or “training” data
– Generalizing = building “statistical models” to capture correlations
– Process = ML is never done, you must keep validating & refitting models
• Simple ML workflow:
– Explore data
– FIT models based on data
– APPLY models in production
– Keep validating models
“All models are wrong, but some are useful.”
- George Box

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3 Types of Machine Learning
1. Supervised Learning: generalizing from labeled data

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3 Types of Machine Learning
2. Unsupervised Learning: generalizing from unlabeled data

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3 Types of Machine Learning
3. Reinforcement Learning: generalizing from rewards in time
Leitner System Recommender systems

10. Copyright © 2016 Splunk Inc.
ML Use Cases

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IT Ops: Predictive Maintenance
1. Get resource usage data (CPU, latency, outage reports)
2. Explore data, and fit predictive models on past / real-time data
3. Apply & validate models until predictions are accurate
4. Forecast resource saturation, demand & usage
5. Surface incidents to IT Ops, who INVESTIGATES & ACTS
Problem: Network outages and truck rolls cause big time & money expense
Solution: Build predictive model to forecast outage scenarios, act pre-emptively & learn

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Security: Find Insider Threats
Problem: Security breaches cause big time & money expense
Solution: Build predictive model to forecast threat scenarios, act pre-emptively & learn
1. Get security data (data transfers, authentication, incidents)
2. Explore data, and fit predictive models on past / real-time data
3. Apply & validate models until predictions are accurate
4. Forecast abnormal behavior, risk scores & notable events
5. Surface incidents to Security Ops, who INVESTIGATES & ACTS

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Business Analytics: Predict Customer Churn
Problem: Customer churn causes big time & money expense
Solution: Build predictive model to forecast possible churn, act pre-emptively & learn
1. Get customer data (set-top boxes, web logs, transaction history)
2. Explore data, and fit predictive models on past / real-time data
3. Apply & validate models until predictions are accurate
4. Forecast churn rate & identify customers likely to churn
5. Surface incidents to Business Ops, who INVESTIGATES & ACTS

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Summary: The ML Process
Problem: <Stuff in the world> causes big time & money expense
Solution: Build predictive model to forecast <possible incidents>, act pre-emptively & learn
1. Get all relevant data to problem
2. Explore data, and fit predictive models on past / real-time data
3. Apply & validate models until predictions are accurate
4. Forecast KPIs & notable events associated to use case
5. Surface incidents to X Ops, who INVESTIGATES & ACTS
Operationalize

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How To: Operationalize Machine Learning
• Analyst outcomes are DATA to learn from!
• Reinforcement learning is key
• Reduce false positives:
– Recommend events/incidents to analyst
– Record analyst outcomes, use as model input
– Optimize for true positives & against false positives
• Reward analysts for good analysis:
– Give bonuses for successful investigations
– Carrots not sticks
• Reward machines for good learning:
– Calibrate data, risk/rewards, analyst outcome

16. Copyright © 2016 Splunk Inc.
ML with Splunk

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Splunk User Behavior Analytics (UBA)
• ~100% of breaches involve valid credentials (MandiantReport)
• Need to understand normal & anomalous behaviors for ALL users
• UBA detects Advanced Cyberattacks and Malicious Insider Threats
• Lots of ML under the hood:
– Behavior Baselining & Modeling
– Anomaly Detection (30+ models)
– Advanced Threat Detection
• E.g., Data Exfil Threat:
– “Saw this strange login & data transfer
for user mpittman at 3am in China…”
– Surface threat to SOC Analysts

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Machine Learning in Splunk ITSI
Adaptive Thresholding:
• Learn baselines & dynamic thresholds
• Alert & act on deviations
• Manage for 1000s of KPIs & entities
• Stdev/Avg, Quartile/Median, Range
Anomaly Detection:
• Find “hiccups” in expected patterns
• Catches deviations beyond thresholds
• Uses Holt-Winters algorithm

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ML Toolkit & Showcase
• Splunk Supported framework for building ML Apps
– Get it for free: http://tiny.cc/splunkmlapp
• Leverages Python for Scientific Computing (PSC) add-on:
– Open-source Python data science ecosystem
– NumPy, SciPy, scitkit-learn, pandas, statsmodels
• Showcase use cases: Predict Hard Drive Failure, Server Power
Consumption, Application Usage, Customer Churn & more
• Standard algorithms out of the box:
– Supervised: Logistic Regression, SVM, Linear Regression, Random Forest, etc.
– Unsupervised: KMeans, DBSCAN, Spectral Clustering, PCA, KernelPCA, etc.
• Implement one of 300+ algorithms by editing Python scripts

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Clustering:
• kmeans, cluster
• K-means
• DBSCAN
• Birch
• Spectral Clustering
Splunk ML Algorithms
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Unsupervised Supervised
ContinuousCategorical
Classification:
• Logistic Regression
• Support Vector Machine
• Naïve-Bayes (Gaussian, Bernoulli)
• RandomForestClassifier
• KNN, Trees
Regression:
• Linear Regression
• Polynomial Regression
• ElasticNet
• Ridge
• Lasso
• RandomForestRegr.
Dimensionality
reduction:
• PCA
• KernelPCA
Association Analysis
• Apriori
• FP-Growth
Hidden Markov Model
predict
outliers
anomalies
anomalydetection
Vectorization:
• TFIDF
• Decision Trees
SPL command ML Toolkit App v1.0

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New Goodness in ML Toolkit v1.0
• New Algorithms (Random Forest, Lasso, Kernel PCA, and more…)
• More use cases to explore
• Support added for Search Head Clustering
• Removed 50k limit on model fitting
• Sampling for training/test data
• Guided ML via a ML Assistant aka Model / Query Builder
• Install on 6.4 Search Head

22. Copyright © 2016 Splunk Inc.
Building ML Apps

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Analysts Business Users
1. Get Data & Find Decision-Makers
2
IT Users
ODBC
SDK
API
DB Connect
Look-Ups
Ad Hoc
Search
Monitor
and Alert
Reports /
Analyze
Custom
Dashboards
GPS /
Cellular
Devices Networks Hadoop
Servers Applications Online
Shopping Carts
Analysts Business Users
Structured Data Sources
CRM ERP HR Billing Product Finance
Data Warehouse
Clickstreams

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2. Explore Data, Build Searches & Dashboards
• Start with the Exploratory Data Analysis phase
– “80% of data science is sourcing, cleaning, and preparing the data”
– Tip: leverage ITSI KPIs – lots of domain knowledge
• For each data source, build “data diagnostic” dashboard
– What’s interesting? Throw up some basic charts.
– What’s relevant for this use case?
– Any anomalies? Are thresholds useful?
• Mix data streams & compute aggregates
– Compute KPIs & statistics w/ stats, eventstats, etc.
– Enrich data streams with useful structured data
– stats count by X Y – where X,Y from different sources
– Build new KPIs from what you find

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3. Fit, Apply & Validate Models
• ML SPL – New grammar for doing ML in Splunk
• fit – fit models based on training data
– [training data] | fit LinearRegression costly_KPI
from feature1 feature2 feature3 into my_model
• apply – apply models on testing and production data
– [testing/production data] | apply my_model
• Validate Your Model (The Hard Part)
– Why hard? Because statistics is hard! Also: model error ≠ real world risk.
– Analyze residuals, mean-square error, goodness of fit, cross-validate, etc.
– Take Splunk’s Analytics & Data Science Education course

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4. Predict & Act
• Forecast KPIs & predict notable events
– When will my system have a critical error?
– In which service or process?
– What’s the probable root cause?
• How will people act on predictions?
– Is this a Sev 1/2/3 event? Who responds?
– Deliver via Notable Events or dashboard?
– Human response or automated response?
• How do you improve the models?
– Iterate, add more data, extract more features
– Keep track of true/false positives

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5. Operationalize Your Models
• Operationalizing closes the loop of the ML Process:
1. Get data
2. Explore data & fit models
3. Apply & validate models
4. Forecast KPIs & events
5. Surface incidents to Ops team
• When you deliver the outcome, keep track of the response
– Human-generated response (detailed journal logs, etc)
– Machine-generated response (workflow actions, etc)
– External knowledge (closed tickets data, DB records, etc)
• Then operationalize: feed back Ops analysis to data inputs, repeat
• Lots of hard work & stats, but lots of value will come out.
Operationalize

28. Copyright © 2016 Splunk Inc.
Show me the ML!

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Next Steps with Splunk ML
• Reach out to your Tech Team! We can help architect ML workflows.
• Lots of ML commands in Core Splunk (predict, anomalydetection, stats)
• ML Toolkit & Showcase – available and free, ready to use
– Get it for free: http://tiny.cc/splunkmlapp
• Splunk UBA: Applied ML for Security
– Unsupervised learning of Users & Entities
– Surfaces Anomalies & Threats
• Splunk ITSI: Applied ML for ITOA use cases
– Manage 1000s of KPIs & alerts
– Adaptive Thresholding & Anomaly Detection
• ML New Product Initiative (NPI) Program:
– Connect with Product & Engineering teams - mlprogram@splunk.com

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