Predictive Analytics using Teradata Aster Scoring SDK

Transcription

1 Predictive Analytics using Teradata Aster Scoring SDK Faraz Ahmad Software Engineer, Teradata #TDPARTNERS16 GEORGIA WORLD CONGRESS CENTER

2 At Teradata, we believe. Analytics and data unleash the potential of great companies 2

3 Outline Motivation (Use cases) Current Framework (without Scoring) Scoring Framework Design Details Execution Flow AMLGenerator Scorer API Performance Results 3

4 Customer Use Cases Top verticals include Telecom, Retail, Banking Churn Reduction Reduce churn for telecom and other service providers by connecting users with loyalty specialists before they churn. Fraud Prevention Prevent fraud before transactions are approved for banks and credit card companies. Online Recommendations Provide personalized recommendations and upsell offers for customers based on current interests. Price optimization Offer dynamic pricing for products based on site traffic, demand and competition. 4

5 Current Framework (Analytic Flow) Training Data Queries (Test Data) Requests Appropriate Action Analytics (Model Builder) Model Analytics (Predictor) Response Score ASTER FRAMEWORK (BATCH-MODE PROCESSING) 5

6 Scoring Framework Training Data Queries (Test Data) Requests Appropriate Action 6 Analytics (Model Builder) Model ASTER FRAMEWORK Scorer Response Scores USER FRAMEWORK

7 Scoring Workflow Train model as you do today in Aster. Use AMLGenerator function to create real-time model. Export real-time model to user framework. Score queries using scorer API based on the exported model. Training Data SQLMR function Queries (Test Data) Appropriate action Analytics (Model Builder) Model AML Generator AML Model Scorer requests response Scores 7

8 AML (Aster Model Language) XML-based format. Contents Model used for scoring. Configuration arguments. Configuration schema. Configuration for prescoring data preparation. 8

9 Scorer Execution Flow AML File Model Type Model Definition Model Data Request Parameters Request Definition Requests Requests Initialize Responses Instantiation Configure Score Response 9 (1) (2) (3)

10 Scorer API Usage Example initialization configuration scoring 10

11 Pipelining Multiple Scorers Analytics (Model Builder) Model1 Queries (Test Data) Requests1 Scores1 Scorer1 Requests2 Action Analytics (Model Builder) Model2 Scorer2 Scores2 11

12 Scorer Model Support (AA6.21) Regression (Generalized Linear Model) Decision Tree (Single Decision Tree, Random Forests) Probabilistic Classification (Naive Bayes) Statistical Analysis (SVM, CoxPH) Text Analysis (Text Parser, Text Tokenizer, Text Tagging, Naïve Bayes Text, Extract Sentiment, LDA Inference) 12

13 Documentation (Scorer Package) 13

14 Documentation (Scorer Class) 14

15 Performance Evaluation Requests Web Interface (jsp) Responses Requests Responses Web Server (tomcat) Scorer Scorer Scorer Scorer Intel Xeon 3.20 GHz RAM: 16GB Apache Tomcat 8 JVM size: 1GB 15

16 Churn Use Case Naive Bayes Single Decision Tree Naive Bayes Model Customer Id NB Scorer Requests1 Requests2 Search Keyword Significance Single Decision Tree Model SDT Scorer Scores Appropriate Action ASTER FRAMEWORK USER FRAMEWORK 16

17 Performance Churn Use Case 17 Naive Bayes Model size 4559 bytes Request size 1067 bytes Number of attributes 104 1,000,000 requests ms / query Single Decision Tree Tree Depth 5 levels Model size 2368 bytes Request size 1542 bytes Number of attributes 16 1,000,000 requests ms / query

18 Performance Fraud Use Case Generalized Linear Model Family Logistic Link logit Model size 2602 bytes Request size 21 bytes Number of attributes 20 1,000,000 requests ms / query Teradata Aster Scoring SDK provides a realtime framework for predictive analytics. 18

19 Thank You Questions/Comments Follow Me Faraz_Aster Rate This Session # 345 with the PARTNERS Mobile App Remember To Share Your Virtual Passes 19

20 At Teradata. We empower companies to achieve high-impact business outcomes through analytics at scale on an agile data foundation 20

21 Backups 21

22 Motivation Online Churn cancel service Search CHURN PROPENSITY 22

23 Motivation Online Fraud bank transactions - $2,000 - $10,000 FRAUD SUSPICION 23

24 Motivation Online Recommendation RECOMMENDATION UPSELL 24