Many high-tech industries rely on machine-learning systems in production environments to automatically classify and respond to vast amounts of incoming data. Despite their critical roles, these systems are often not actively monitored. When a problem first arises, it may go unnoticed for some time. Once it is noticed, investigating its underlying cause is a time-consuming, manual process. Wouldn’t it be great if the model’s output were automatically monitored? If they could be visualized, sliced by different dimensions? If the system could automatically detect performance degradation and trigger alerts? In this presentation, we describe our experience from building such a core machine-learning services: Model Evaluation. Our service provides automated, continuous evaluation of the performance of a deployed model over commonly-used metrics like the area-under-the-curve (AUC), root-mean-square-error (RMSE) etc. In addition, summary statistics about the model’s output, their distributions are also computed. The service also provides a dashboard to visualize the performance metrics, summary statistics and distributions of a model over time along with REST APIs to retrieve these metrics programmatically. These metrics can be sliced by input features (e.g. Geography, Product type) to provide insights into model performance over different segments. The talk will describe various components that are required in building such a service and metrics of interest. Our system has a backend component built with spark on Azure Databricks. The backend can scale to analyze TBs of data to generate model evaluation metrics. We will talk about how we modified Spark MLLib for computing AUC sliced by different dimensions and other optimizations in Spark to improve compute and performance. Our front-end and middle-tier, built with Docker and Azure Webapp provides visuals and REST APIs to retrieve the above metrics. This talk will cover various aspects of building, deploying and using the above system.

1. WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics

2. Vijay Srivastava & Deepak Pai
Adobe Inc.
Continuous Evaluation of
Deployed Models in
Production
#UnifiedDataAnalytics #SparkAISummit

3. Agenda
3
Overview: ML Core Services
• Services & Architecture
Continuous Model Evaluation
• Why do we need it?
• Evaluation pipeline
• Evaluation metrics - AUC trends, Score distribution
Spark ML AUC optimization

4. • ML Core Services
• Major Services:
– Data Quality, Summarization and Visualization
– Continuous Model Evaluation
– Causal Inference
– Model Interpretation
4
Core ML Services

5. 5
Platform (Adobe Experience Platform / Azure / AWS)
ML Core Services
Sensei
Service-1
(API)
Sensei
Service-2
(API)
Sensei
Service-N
(API)
Data
Scientists
(UI)
Architecture

6. Continuous Model Evaluation
6

7. Continuous Model Evaluation – Why?
• Production models
– Not actively monitored
• Reactive approach
– Model are looked only on encountering accuracy and performance issues
• Need to know when to retrain the model
• Detect performance drops.
7~~`

8. • Automate performance evaluation/monitoring of models.
• Score and Label distributions.
• Visualize performance metrics sliced by attributes.
• Detect performance drops and trigger alerts.
8
Continuous Model Evaluation – How?

9. Core ML - Model Configuration
• Configure Model and linked data sources
– Configure data source storing model output scores (Prediction Source)
– Configure data source storing ground-truth (Label Source)
– Configure evaluation metrics (e.g. AUC, RATE)
• Define features to slice/visualize metrics evaluations.
9

10. Core ML - Model Configuration
10
data source path
azure blob / s3 bucket / {internal location}
join key
slicing features
evaluation metric

11. Back end process
11
Identify new
files Read files Join data
Compute
Metrics

12. Visualization Dashboard – AUC trends
12

13. AUC trends Comparison – Multiple Models
13

14. Visualization Dashboard – Average Score Trends
14

15. 15
Visualization Dashboard – Score Distribution by Deciles

16. AUC Computation Optimization
16

17. AUC Computation: Basic Algorithm
• Input: Sequence of (score, label)
q E.g. [ (0.9, ‘T’ ), (0.8, ‘T’), (0.77, ‘T’),(0.5, ‘F’), …..]
• AUC Computation:
q Sort the data by score in descending order
q Use each score as classification threshold to
predict labels and compute confusion matrix ( and
True Positive Rate(TPR) and False Positive
Rate(FPR)).
q Plot ROC curve using TPR and FPR as
coordinates.
q Compute AUC using trapezoidal rule.
17
FP Rate
TPRate
0 1
1
threshold
ROC Curve
trapezoid

18. AUC Computation
Spark ML Lib Binary Classification Metrics
• Spark ML Lib Class:
‘org.apache.spark.mllib.evaluation.BinaryClassificationMetrics’
• Input: RDD [(score, label)]
q E.g. [ (0.9, ‘T’ ), (0.8, ‘T’), (0.77, ‘T’),(0.5, ‘F’), …..]
• Steps:
q Sort by score
q Bucketize
q Use Spark map-partition to compute cumulative count
q Compute Cumulative Confusion Matrix
q Compute AUC using trapezoidal rule.
18

19. AUC Computation: Challenges in existing
implementation
Ø Computing AUC for each feature value requires:
• Filter data for each distinct feature value.
e.g. Feature: Country,
Values: [USA, IN, NL, FR,….]
• Multiple calls to spark ML Lib AUC implementation.
Ø Time complexity proportional to feature cardinality
19

20. AUC Computation: Example using existing Binary Classification
Implementation
20
Original RDD
[UUID, Country, Product,…….,Score, Conversion Label]
[0001, US , Photoshop,….,0.712, ‘Yes’]
[0002, IN , After Effects,…,0.778, ‘Yes’]
[0003, US , After Effects,…,0.812, ‘Yes’]
[0004, IN , Photoshop,…,0.678, ‘No’]
…….
Country = US Country = IN Product = Photoshop Product = After Effects
[0001, US , Photoshop,….,0.712, ‘Yes’]
[0003, US , After Effects,…,0.812, ‘Yes’]
…….
[0002, IN , After Effects,…,0.778, ‘Yes’]
[0004, IN , Photoshop,…,0.678, ‘No’]
…….
[0001, US , Photoshop,….,0.712, ‘Yes’]
[0004, IN , Photoshop,…,0.678, ‘No’]
…….
[0002, IN , After Effects,…,0.778, ‘Yes’]
[0003, US , After Effects,…,0.812, ‘Yes’]
…….
AUC (US) = 0.77 AUC (IN) = 0.71 AUC (Photoshop) = 0.69 AUC (After Effects) = 0.75
Binary Classification
Metrics
area Under ROC
Binary Classification
Metrics
area Under ROC
Binary Classification
Metrics
area Under ROC
Binary Classification
Metrics
area Under ROC

21. AUC Computation: Updated implementation
• Extends “org.apache.spark.mllib.evaluation.BinaryClassificationMetrics”
• RDD[(score, label)] => RDD[(attribute, attribute-value), (score, label)]
e.g. [ [0001, US , Photoshop,….,0.712, ‘Yes’], ...] =>
[ [ ((country, US ), (0.712, ‘T’)), ((product, ‘Photoshop’), (0.712, ‘T’)) ] , [ ….. ] ]
• Input: RDD [(attribute, attribute-value), (score, label)]
• Output: Map [((attribute, attribute-value), auc-score)]
21

22. AUC Computation: Updated implementation
Steps :
ü Sort by (attribute-value, score)
ü Bucketize by attribute-value.
ü Used Spark map-partition to compute cumulative counts per
attribute-value.
ü Compute Cumulative Confusion Matrix for each attribute
value.
ü Compute AUC for each distinct (attribute, attribute-value)
pair.
22

23. AUC Computation: Example using existing Binary Classification
Implementation
23
Original RDD
[UUID, Country, Product,…….,Score, Conversion Label]
[0001, US , Photoshop,….,0.712, ‘Yes’]
[0002, IN , After Effects,…,0.778, ‘Yes’]
[0003, US , After Effects,…,0.812, ‘Yes’]
[0004, IN , Photoshop,…,0.678, ‘No’]
…….
[0001, US ,0.712, ‘Yes’], [0001, Photoshop,0.712, ‘Yes’], [0001, GLOBAL,0.712, ‘Yes]
[0002, IN , 0.778, ‘Yes’], [0002, After Effects,0.778, ‘Yes’], [0002, GLOBAL,0.778, ‘Yes’]
[0003, US , 0.812, ‘Yes’], [0003, After Effects,0.812, ‘Yes’], [0003, GLOBAL ,0.812, ‘Yes’]
[0004, IN ,0.678, ‘No’], [0004, Photoshop,…,0.678, ‘No’], [0004, GLOBAL ,0.678, ‘No’]
…….
AUC (US) = 0.77
AUC (IN) = 0.71
AUC (Photoshop) = 0.69
AUC (After Effects) = 0.75
AUC (GLOBAL) = 0.78
Core ML Implementation of Binary
Classification Metrics: area Under ROC
Transformed RDD

24. Spark ML BinaryClassificationMetrics: Original Code Snippet
24
Input
Create Curve Point using TPR and FPR
Compute area under roc curve

25. 25
combine & sort data by score
Bucketize data (binning)
partition wise cumulative count
Total count
cumulative confusion matrix

26. 26
combine & sort data by (attribute value, score)
bin size computation based on attribute
value
AUC Computation: Updated implementation
Bucketizing based on attribute value

27. AUC Computation: Updated implementation
Advantages:
v Faster
v Computation is Independent of feature cardinality
27

28. Future:
• Adding support for other evaluation metrics(e.g. Root
Mean Squared Error, LIFT etc.)
• Contribute back to apache spark community by
extending “BinaryClassificationMetrics” to support AUC
computation sliced by feature value.
28

29. Contact:
Vijay Srivastava <vijays@adobe.com>
Staff Data Scientist
Adobe Inc.
Deepak Pai <dpai@adobe.com>
Manager, ML Core Services
Adobe Inc.
Credits:
Joshua Sweetkind-Singer
Principal ML Architect
Shankar Venkitachalam
Data Scientist
THANKS!

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