Deploying Real-Time Decision Services Using Redis with Tague Griffith
3 likes•557 views
The document discusses deploying real-time decision services using Redis and machine learning techniques for various applications such as spam detection and customer segmentation. It details the integration of Redis with machine learning models, emphasizing Redis-ML as a predictive model serving engine that supports advanced functionalities like decision trees and random forests. The document concludes by highlighting the efficiency of using Redis in reducing resource costs for machine learning deployment.
![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](https://image.slidesharecdn.com/3taguegriffith-180614030850/85/Deploying-Real-Time-Decision-Services-Using-Redis-with-Tague-Griffith-13-320.jpg)
![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]](https://image.slidesharecdn.com/3taguegriffith-180614030850/85/Deploying-Real-Time-Decision-Services-Using-Redis-with-Tague-Griffith-20-320.jpg)
Deploying Real-Time Decision Services Using Redis with Tague Griffith
- 1. Tague Griffith, Redis Labs Deploying Real-Time Decision Sevices using Redis #MLSAIS12
- 3. Teaching a computer, by example, an algorithm that is too complex to program
- 4. Machine Learning Problems Pick One of a Set • Spam Detection • Manufacturing defect detection • Handwriting analysis • Decision Trees • Naïve Bayes • Logistic Regression Score or Rank • Recommendations • Likelihood of Purchase • Linear Regression • SVM Classification Regression Group Similar • Find Similar Items • Customer segmentation • Cohort detection • K-Means • K-Nearest Neighbors • Hierarchical Clustering Clustering
- 5. Supervised Learning – Training Spam Classifier #MLSAIS12
- 6. Deploying a Spam Classifier #MLSAIS12
- 7. How do we Build these Boxes ¯_( )_/¯ #MLSAIS12
- 8. • Building high performance and reliable services are hard, isn't there something we can deploy
- 9. Redis - ML
- 10. Typical Spark Application Structure 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 #MLSAIS12
- 11. 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
- 12. REmote DIctionary Server Strings Hashes Lists Sets Bitmaps Hyperlog- logs Sorted Sets Geo- spatial Bitfield
- 13. 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
- 14. 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
- 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 vecto • 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%
- 23. 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 +
- 24. Thank you!