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Copyright	©	2015	Splunk	Inc.
Operationalizing
Machine	Learning
Kelly Feagans
Sr. Sales Engineer
Splunk, Inc.
2
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.
Copyright	©	2015	Splunk	Inc.
Who	Am	I?
Copyright	©	2016	Splunk	Inc.
• Kelly	Feagans	– Data	Centurion,	Splunk	Sr.	
Engineer,	 Splunk	Transparency	Maint.	
Engineer
• ITOps Background
• Vendor	enabled	(dark	side)	in	2000
• Previous	Work:	Network	architecture	&	
capacity	planning,	 Infrastructure	Compliance	
&	Security
• NOT	a	Ph.D.	in	Math	nor	Stats
• (please	be	nice)
• Star	Wars	over	Star	Trek	(better	weapons)
Copyright	©	2015	Splunk	Inc.
The	Why’s	of	Machine	Learning
Copyright	©	2016	Splunk	Inc.
Historical	Data Real-time	Data Statistical	Models
DB,	HDFS,	NoSQL,	Splunk,	etc 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
Splunk
Security	Operations	Center
Network	Operations	Center
Business	Operations	Center
Copyright	©	2015	Splunk	Inc.
What	is	Machine	Learning?
8
Machine	Learning	101:		What	is	it?
• Machine	Learning	is	a	process	for	generalizing	from	examples
– Examples	=	example	or	“training”	data
– Generalizing	=	building	“statistical	models”	to	capture	correlations
– Process	=	never	quite	done,	we	keep	validating	&	refitting	models	for	
increasing	accuracy
• Simple	Machine	Learning	workflow:
– Explore	data
– FIT	models	based	on	data
– APPLY	models	in	production
– Keep	validating	models
“All	models	are	wrong,	but	some	are	useful.”
9
Three	Types	of	Machine	Learning
1.	Supervised Learning:		generalizing	from	labeled data
OR?
Gather	data:
• Dimensions
• Stem	Length
• Color
• Etc.
10
Three	Types	of	Machine	Learning
2.	Unsupervised Learning:		generalizing	from	unlabeled data
Will	my	home	sell?
Gather	data:
• Square	feet
• Levels
• Parks	nearby
• Schools
• Zipcode
• Etc.
11
Three	Types	of	Machine	Learning
3.	Reinforcement	Learning:	generalizing	from	rewards in	time
Recommendation	Engines
Copyright	©	2015	Splunk	Inc.
Machine	Learning	
Use	Cases
13
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
14
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
15
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. Identify	customers	likely	to	churn
5. Surface	incidents	to	Business	Ops,	who	INVESTIGATES	&	ACTS
16
Summary:	The	Machine	Learning	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	&	metrics	associated	to	use	case
5. Surface	incidents	to	X	Ops,	who	INVESTIGATES	&	ACTS	
Operationalize
Copyright	©	2015	Splunk	Inc.
Machine	Learning	with	Splunk!
18
Analysts Business	Users
1.	Get	Data	&	Find	Decision-Makers!
1
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
19
2.	Explore	Data,	Build	Searches	&	Dashboards
• Start	with	the	Exploratory	Data	Analysis	phase
– “80%	of	data	science	is	sourcing,	cleaning,	and	preparing	the	data”	
• 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
20
3.	Get	the	ML	Toolkit	&	Showcase	App
• Get	the	App!	https://splunkbase.splunk.com/app/2890
• Leverages	Python	for	Scientific	Computing (PSC)	add-on:
– Open-source	Python	data	science	ecosystem
– NumPy,	SciPy,	scitkit-learn,	pandas,	statsmodels
• Showcase	use	cases:	Hard	Drive	Failure,	Server	Power	consumption,	
Server	Response	Time,	Application	Usage
• Standard	algorithms out	of	the	box:
– Supervised:	Logistic	Regression,	SVM,	Linear	Regression,	Random	Forest
– Unsupervised: KMeans,	DBSCAN,	Spectral	Clustering	
• Implement	one	of	300+	algorithms	by	editing	Python	scripts
21
4.	Fit,	Apply	&	Validate	Models
• Machine	Learning	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
22
5.	Operationalize	Your	Models
• Remember	the	ML	Process:	
1. Get	data
2. Explore	data	&	fit	models
3. Apply	&	validate	models
4. Forecast	KPIs
5. Surface	incidents	to	Ops	team
• Then	operationalize:	feed	back	Ops	analysis	to	data	inputs,	repeat
• Be	patient,	this	takes	time,	but	lots	of	value	will	come	out.
Operationalize
Copyright	©	2015	Splunk	Inc.
The	Splunk	Machine	Learning	App!
24
Sneak	Peak	Recap:	What’s	new	in	GA
• 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
25
What	Do	I	Do	Next?
• Reach	out	to	your	Tech	Team!	We	can	help	architect	Machine	Learning	
(ML)	workflows.
• Lots	of	ML	commands	in	Core	Splunk	(predict,	anomalydetection,	stats)
• ML	Toolkit	&	Showcase	– available	and	free,	ready	to	use	..	GO!
• Splunk	ITSI:	Applied	ML	for	ITOA	use	cases
– Manage	1000s	of	KPIs	&	alerts
– Adaptive	Thresholding	&	Anomaly	Detection
• Splunk	UBA:	Applied	ML	for	Security
– Unsupervised	learning	of	Users	&	Entities
– Surfaces	Anomalies	&	Threats
• ML	Customer	Advisory	Program:	
– Connect	with	Product	&	Engineering	teams	- mlprogram@splunk.com
26
SEPT	26-29,	2016
WALT	DISNEY	WORLD,	ORLANDO
SWAN	AND	DOLPHIN	RESORTS
• 5000+		IT	&	Business	Professionals
• 3	days	of	technical	content
• 165+	sessions	
• 80+	Customer	Speakers
• 35+	Apps	in	Splunk	Apps	Showcase
• 75+	Technology	Partners
• 1:1	networking:	Ask	The	Experts	and	Security	
Experts,	Birds	of	a	Feather	and	Chalk	Talks
• NEW	hands-on	labs!	
• Expanded	show	floor,	Dashboards	Control	
Room	&	Clinic,	and	MORE!	
The	7th Annual	Splunk	Worldwide	Users’	Conference
PLUS	Splunk	University
• Three	days:	Sept	24-26,	2016
• Get	Splunk	Certified	for	FREE!
• Get	CPE credits	for	CISSP,	CAP,	SSCP
• Save	thousands	 on	Splunk	education!
#splunkconf2016
Copyright	©	2015	Splunk	Inc.
Thank	you!

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