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Home	of	Redis
Serving	Predictive	Models	with	Redis	
Tague	Griffith
Head	of	Developer	Advocacy
2
REmote DIctionary Server
Strings Hashes Lists
Sets Bitmaps
Hyperlog-
logs
Sorted	
Sets
Geospatial Bitfield
3
A	Quick	Recap	of	Redis
Key
"I'm a Plain Text String!"
{ A: “foo”, B: “bar”, C: “baz” }
Strings / Bitmaps / BitFields
Hash Tables (objects!)
Linked Lists
Sets
Sorted Sets
Geo Sets
HyperLogLog
{ A , B , C , D , E }
[ A → B → C → D → E ]
{ A: 0.1, B: 0.3, C: 100, D: 1337 }
{ A: (51.5, 0.12), B: (32.1, 34.7) }
00110101 11001110 10101010
Why	Machine	Learning
5
Teaching	a	computer	by	example	to	learn	an	
algorithm	that	is	too	complex	to	program
6
Machine	Learning	Problems
Pick	One	of	a	Set
• Spam	Detection
• Manufacturing	defect	
detection
• Handwriting	analysis
• Decision	Trees/Forests
• Naïve	Bayes
Score	or	Rank
• Recommendations
• Likelihood	of	Purchase
• Linear	Regression
• Logistic	Regression
Classification Regression
Group	Similar
• Find	Similar	Items
• Customer	segmentation
• Cohort	detection
• K-Means
• Hierarchical	Clustering
Clustering
7
Supervised	Learning	– Training	Spam	Classifier
Mail Spam Mail MailMail
Mail
Spam
SpamSpam Mail
MailSpam
Spam
Mail Spam
8
Deploying	a	Spam	Classifier
8
Spam
Mail
Spam Spam
9
How	do	we	Build	these	Boxes
¯_(ツ)_/¯
10
Typical	Spark	Application	Structure
10
Spark	Training
Data	is	loaded	into	Spark Model	is	saved	in	files
File	System Custom	Server
Model	is	loaded	to	your	
custom	app
Serving	Client
Client	App
11
Building	high	performance	and	reliable	
services	are	hard,	isn't	there	something	we	can	
deploy
Redis	- ML
13
Redis	Modules
• Any	C/C++	program	can	now	run	on	Redis
• Use	existing	or	add	new	data-structures	
• Enjoy	simplicity,	infinite	scalability	and	high	availability	while	
keeping	the	native	speed	of	Redis	
• Can	be	created	by	anyone
New Capabilities
New Commands
New Data Types
14
Redis-ML:	Predictive	Model	Serving	Engine
• Predictive	models	as	native	Redis	types
• Perform	evaluation	directly	in	Redis
• Store	training	output	as	“hot	model”
Spark	Training
Data	loaded	into	Spark Model	is	saved	in	
Redis-ML
Redis-ML
Serving	Client
Client	
App
Client	
App
Client	
App
Any	Training	
Platform
15
Redis	ML	Module
Redis	Module
Tree	Ensembles
Linear	Regression
Logistic	Regression
Matrix	+	Vector	Operations
More	to	come...
16
Random	Forest	Model
• A	collection	of	decision	trees
• Supports	classification	&	regression
• Splitter	Node	can	be:
◦ Categorical	(e.g.	day	==	“Sunday”)
◦ Numerical	(e.g.	age	<	43)
• Decision	is	taken	by	the	majority	of	decision	trees
17
Classic	Tree	Problem:	Titanic	Survival	
YES
Sex	=
Male	?
Age	<	
9.5?
Sibps >	
2.5?
Survived
Died
SurvivedDied
NO
• Passenger	Data	encoded	as	feature	vectors
• ML	Algorithm	learns	the	tree	rules	
• ID3,	CART	(RPART),	etc.
• Tree	rules	used	to	infer	results
18
Titanic	Survival:	Random	Forest
YES
Sex	=
Male	?
Age	<	
9.5?
*Sibps	>	
2.5?
Survived
Died
SurvivedDied
NO YES
Country=
US?
State	=	
CA?
Height>	
1.60m?
Survived
Died
SurvivedDied
NO YES
Weight<	
80kg?
I.Q<100?
Eye	color	
=blue?
Survived
Died
SurvivedDied
NO
Tree	#1 Tree	#2 Tree	#3
19
Who	Would	Survive	the	Titanic
John:
• Male,	34,	
• Married	w/	2	kids	(Sibps=3)
• New	York,	USA
• 1.78m,	78kg	
• 110	iq
• Blue	eyes
Mathew:
• Male,	6	
• 3	Sisters	(Sibps=3)
• New	York,	USA
• 1.06m,	22.7	kg
• 100	iq
• Brown	eyes
Let's	use	our	forest	to	find	out
20
Redis:	Forest	Data	Type
Add	nodes	to	a	tree	in	a	forest:
Perform	classification/regression	of	a	feature	vector:
ML.FOREST.ADD <forestId> <treeId> <path>
[ [NUMERIC|CATEGORIC] <splitterAttr> <splitterVal> ] |
[LEAF] <predVal>
ML.FOREST.RUN <forestId> <features>
[CLASSIFICATION|REGRESSION]
21
Real	World	Challenge
• Ad	serving	company
• Need	to	serve	20,000	ads/sec	@	50msec	data-center	latency
• Runs	1k	campaigns	→	1K	random	forest	
• Each	forest	has	15K	trees
• On	average	each	tree	has	7	levels	(depth)
22
Ad	Serving	costs:	Homegrown	v.	Redis
Homegrown
1,247 x c4.8xlarge 35 x c4.8xlarge
Cut computing infrastructure
by 97%
22
23
Redis	ML	with	Spark	ML
Random Forest; 1,000 forests @ 15,000 trees
Classification	Time	Over	Spark
13x	Faster
Movie	Classification	Demo
25
The	Tools
Transform:	
25
Train:
Classify:	
+
Containers:
26
Inference	Model
27
Step	1:	Get	The	Data
Download	and	extract	the	MovieLens	100K	Dataset
The	data	is	organized	in	separate	files:
• Ratings:	user	id	|	item	id	|	rating	(1-5)	|	timestamp
• Item	(movie)	info:	movie	id	|	genre	info	fields	(1/0)
• User	info:	user	id	|	age	|	gender	|	occupation
Our	classifier	should	return	the	expected	rating	(from	1	to	5)	a	user	would	give	the	movie	in	question
28
Step	2:	Transform
28
The	training	data	for	each	movie	should	contain	1	line	per	user:
• class	(rating	from	1	to	5	the	user	gave	to	this	movie)	
• user	info	(age,	gender,	occupation)
• user	ratings	of	other	movies	(movie_id:rating	...)
• user	genre	rating	averages	(genre:avg_score	...)
Run	gen_data.py	to	transform	the	files	to	the	desired	format
29
Step3:	Train	and	Load	to	Redis
//	Create	a	new	forest	instance
val rf = new
RandomForestClassifier().setFeatureSubsetStrategy("auto").setLabelCol("indexedLabel").setFeat
uresCol("indexedFeatures").setNumTrees(500)
…..
// Train model
val model = pipeline.fit(trainingData)
…..
val rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel]
// Load the model to redis
val f = new Forest(rfModel.trees)
f.loadToRedis(”movie-10", "127.0.0.1")
30
Step	4:	Execute	inference	in	Redis
Redis-ML
+
Spark	
Training
Client	App
31
Summary
• Train	with	Spark,	Serve	with	Redis
• 97%	resource	cost	serving
• Simplify	ML	lifecycle
• Redise	(Cloud	or	Pack):
‒Scaling,	HA,	Performance	
‒PAYG	– cost	optimized	
‒Ease	of	use
‒Supported		by	the	teams	who	created Spark	and	
Redis
Spark	Training
Data	loaded	into	Spark Model	is	saved	in	
Redis-ML
Redis-ML
Serving	Client
Client	
App
Client	
App
Client	
App
+
32
Where	to	Find	Me
@tague
https://github.com/tague
tague@redislabs.com

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