Predictive intelligence from machine learning has the potential to change everything in our day to day experiences, from education to entertainment, from travel to healthcare, from business to leisure and everything in between. Modern ML frameworks are batch by nature and cannot pivot on the fly to changing user data or situations. Many simple ML applications such as those that enhance the user experience, can benefit from real-time robust predictive models that adapt on the fly. Join this session to learn how common practices in machine learning such as running a trained model in production can be substantially accelerated and radically simplified by using Redis modules that natively store and execute common models generated by Spark ML and Tensorflow algorithms. We will also discuss the implementation of simple, real-time feed-forward neural networks with Neural Redis and scenarios that can benefit from such efficient, accelerated artificial intelligence. Real-life implementations of these new techniques at a large consumer credit company for fraud analytics, at an online e-commerce provider for user recommendations and at a large media company for targeting content will also be discussed.

1. Home of Redis
Real-time Machine Learning
Cihan Biyikoglu
VP of Product Management
and surviving Titanic…
Wed June 7th – Room#2011 @ SparkSummit2017

2. Agenda
o Intro to Redis and Redis Labs
o Real-time Analytics with Redis
o Deep Dive into Machine Learning with Redis
o QA
2

3. Intro to Redis Labs
3

4. Redis Labs – Home of Redis
Founded in 2011
HQ in Mountain View CA, R&D center in Tel-Aviv IL
The commercial company behind Open Source Redis
Provider of the Redis Enterprise (Redise) technology,
platform and products

5. Redise Cloud Private
Redis Labs Products
Redise Cloud Redise Pack ManagedRedise Pack
SERVICES SOFTWARE
Fully managed Redise service in
Virtual Private Clouds (VPC) within
Amazon (AWS), Microsoft Azure,
Google Cloud,
IBM Softlayer
Fully managed Redise service on
hosted servers within public clouds:
Amazon (AWS), Microsoft Azure,
Google Cloud, IBM Softlayer,
Heroku, Cloud Foundry &
RedHat OpenShift
Downloadable Redise software for
any enterprise datacenter or cloud
environment
Fully managed Redise software in
any private data center
&& &

6. Mature and Stable Technology & Products
250K+ 600+
100 + 1350+
1,000+
DATABASES RUN
OVER 3 YEARS
NEW DATABASES
CREATED EVERY DAY
MAN-YEARS OF ENTERPRISE
REDIS TECHNOLOGY
DEVELOPMENT
CLOUD NODE FAILURE AND
OUTAGES EVENTS
SURVIVED WITH NO DATA
LOSS
GRANTED AND PENDING
PATENTS
DEDICATED REDIS
ENGINEERS

7. Intro to Redis

8. Redis Architecture
- Single Threaded, In-
memory Engine with
Persistence
- “Lock Free” architecture
for fast execution
- In-memory, optimized for
high speed access
- Persistence with AOF or
Snapshot disk durability
“Strings”
“Hash”
“List”
“Sorted Set”
“Sets”
“Module Types”
-
…
…
Key
Key
Key
Key
Key
Key
Key
Key
Key
DISK
Storage Space
Connection Handler
Command Parser
Expiry EvictionModules Dispatcher
Process Space
Disk IO (AOF, Snapshots)Command Dispatcher
Background Services
Replication
Listener
Redis Event Loop

9. Redise Technology :: Cluster Architecture
Redise
Cluster Architecture
• Shared nothing cluster architecture
◦ Single node type for simple scalability
• Fully compatible with open source
commands & data structures
◦ Simply change your Redis application
connection endpoint to Redise

10. Why Redis?
Simplicity
(through Data Structures)
Extensibility
(through Redis Modules)
Performance
ListsSorted Sets
Hashes Hyperlog-logs
Geospatial
Indexes
Bitmaps
SetsStrings
Bit field

11. A Quick Lap Around 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
ListsSorted Sets
Hashes Hyperlog-logs
Geospatial
Indexes
Bitmaps
SetsStrings
Bit field

12. Modules : A Revolutionary Approach
o Native Extensibility in C, C++, Go, Python
• Add your own structures & methods
o 50+ created so far
• More on Next Slide…

13. Modules : A Revolutionary Approach
Adapt your database to your data, not the other way around
Secure way to store data in
Redis via encrypt/decrypt with
various Themis primitives
Time Series Graph
Time series values aggregation
in Redis
Crypto Engine Wrapper
Graph database on Redis
based on Cypher language
Based on Generic Cell Rate
Algorithm (GCRA)
Rate Limiter
ReJSON
Secondary Index/RQL
JSON Engine on Redis.
Pre-released
Indexing + SQL -like syntax for
querying indexes.
Pre-released
Neural Redis Redis-ML RediSearch
Full Text Search Engine in RedisMachine Learning Model
Serving
Simple Neural Network Native
to Redis

14. Machine Learning with Redis

15. #1

16. Redis in Machine Learning
REDIS-ML
• Models can be stored, retrieved and
updated natively with Redis-ML
• Accelerates predictions serving for
complex ML model by x100
• Unified and simplified ML serving
operation – HA, Scale, Perf and more
Save Machine Learning
ModelRead Training Data
Analytics
Apps
Serve Predictions and
Recommendations
Train the Machine Learning Model
1
2
3
4

17. Redis ML

18. The Machine / Deep Learning (ML/DL) World
(1) Training
(3) Serving the model
(2) Generate ML
Model

19. Redis ML with Spark ML
Random Forest; 1,000 forests @ 15,000 trees
Classification Time Over Spark
13x Faster

20. Try it Yourself!
databricks Notebook:
http://bit.ly/sparkredisml

21. The Machine / Deep Learning (ML/DL) World
(1) Training
(3) Serving the model
Homegrown
(2) Generate ML
Model

22. ML Models Serving Challenges
o But then…
1. ML Models are becoming bigger in size as they get more precise and complex!
2. Serving recommendations in mission critical apps - Scaling, Performance, HA & DR…
3. How do you manage multiple model types (Random Forest, Gradient Boosted Trees,
Logistic Regression, etc.)
4. How do you manage multiple versions of each model
5. How do you upgrade a model across so many machines
6. What if the training and serving apps are written in different languages
7. And so on….
Homegrown

23. Real World Challenge
Ad Serving
• 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)
Homegrown

24. Large/Accurate Models are Expensive to Serve !
Item Calculation Total
Random Forest
ops/sec
20K (ads/sec) x
1K (forests) x
15K(trees) x
7 x 0.5 (levels)
1.05 trillion ops/sec
Max ops/sec on the
strongest AWS instance
vcore
2.6Ghz x
0.9 (OS overhead) x
0.1 (10 lines of code per ops) x
0.1 (Java overhead)
23.4 million ops/sec
# of vcores needed 1.1 trillion / 23.4 million 44,872 vCores
# of c4.8xlarge
instances needed
44,872 / 36 1,247 Instances
Total Cost
in Reserved
Instances
1,247 x 9213 ~$11.5M/year

25. The Machine / Deep Learning (ML/DL) World
(1) Training
(3) Serving the model
Homegrown
(2) Generate ML
Model

26. Ads Model Serving: Homegrown vs. Redise + ML
Homegrown
1,247 x c4.8xlarge 35 x c4.8xlarge
Cut computing infrastructure
by 97%

27. More Details
o Catch the Video from Spark Summit 2017!
Building a Large Scale Recommendation Engine
with Spark and Redis-ML
Shay Native – Redis Labs

28. #2

29. Redis in Machine Learning
NEURAL
REDIS
• Simple neural network as a native
data type for Redis
• Training and classification in Redis
with simple, intuitive APIs in real-
time
Save Machine Learning
ModelRead Training Data
Analytics
Apps
Serve Predictions and
Recommendations
Train the Machine Learning Model
1
2
3
4

30. Neural Redis

31. Neural Redis
o A very simple to use API.
o Automatic data normalization.
o Online training of neural networks in different threads.
o Ability to use the neural network while the system is training it (we
train a copy and only later merge the weights).
o Fully connected neural networks using the RPROP (Resilient back
propagation) learning algorithm.
Get Started!
> redis-server --loadmodule /path/to/neuralredis.so
https://github.com/antirez/neural-redis

32. DEMO
Surviving Titanic…

33. Steps
o #1 Load “Titanic Survival” stats
• Passenger Attributes
[Class, Sex, Age, Travel companions – siblings, spouses, parents, children and Fare price]
NR.CREATE mynet CLASSIFIER 9 15 -> 2 DATASET 1000 TEST 500 NORMALIZE
NR.OBSERVE mynet …
o #2 Train
Training done in memory, in real-time…
NR.TRAIN mynet AUTOSTOP
o #3 Predict Survival on Titanic
First Class Passenger [1 0 0], Female [0 1], Age [30], No Companion [0 0], Fare Price [200]
NR.RUN mynet 1 0 0 0 1 30 0 0 200
> % Chance of Death
> % Chance of Survival

34. Real Survival Rates on Titanic!
http://www.anesi.com/titanic.htm

35. DEMO
Surviving Titanic…

36. Agenda
o Intro to Redis and Redis Labs
o Real-time Analytics with Redis
o Deep Dive into Real-time Machine Learning with Redis
o QA
36
Thank You!

37. Resources
o Getting Started with Redis and Redise
• https://hub.docker.com/r/redislabs/redis/
o Getting Started with Redis and Machine Learning
• https://redislabs.com/modules/machine-learning/
o Other Resources
• Redis-ML : https://github.com/RedisLabsModules/redis-ml
• Spark-Redis-ML : https://github.com/RedisLabs/spark-redis-ml
• Neural Redis : https://github.com/antirez/neural-redis
• Databricks Notebook – Spark & Redis : http://bit.ly/sparkredisml

38. Q&A