In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data. In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.

1. Re-imagine Data Monitoring
with whylogs and Apache
Spark
Andy Dang
Co-Founder & Lead Engineer, WhyLabs

2. Outline
ML Data Challenges
How traditional data analysis techniques fail ML
data pipelines
Lightweight Profiling for Big ML
Data
Profiling techniques for detecting data quality
problems
The Open Source whylogs Library
Building the standard for data logging
2

3. Source: Google Cloud AI
3
ML Lifecycle

4. Issues encountered in production (small sample)...
...or it simply doesn’t work, and nobody know why...
● Experiment/production
environment mismatch
● Wrong model version deployed
● Underprovisioned hardware
● Inappropriate hardware
● Latency/SLA issues
● Data permissions misconfigured
● Untracked changes broke prod
● Traffic sent to the wrong model
● Computational instability
● Customers gaming the model
(adversarial attacks)
● PII data exposed
● Expected accuracy doesn’t
materialize
● Pre-processing mismatch in
experiments vs. production
● Retrained on faulty data
● Accuracy improves on one
segment, regresses in others
● Outliers predicted incorrectly
● Bias identified
● Correlation with protected
features
● Overfitting on training/test
● Surge in missing values
● Surge in duplicates
● Poor performance on new
categories
● Poor performance on new
customer segments
● Poor performance on outliers
● Data quality issues affect
accuracy
● Production data doesn’t match
test/training
● Accuracy is decaying over time
● Data drift in inputs
● Concept drift in outputs
● Extreme predictions for out of
distribution data
● Model not generalizing on new
data / new segments
● Major customer behavior shift
4

5. Issues encountered in production (small sample)...
issues caused by data
● Experiment/production
environment mismatch
● Wrong model version deployed
● Underprovisioned hardware
● Inappropriate hardware
● Latency/SLA issues
● Data permissions misconfigured
● Untracked changes broke prod
● Traffic sent to the wrong model
● Computational instability
● Customers gaming the model
(adversarial attacks)
● PII data exposed
● Expected accuracy doesn’t
materialize
● Pre-processing mismatch in
experiments vs. production
● Retrained on faulty data
● Accuracy improves on one
segment, regresses in others
● Outliers predicted incorrectly
● Bias identified
● Correlation with protected
features
● Overfitting on training/test
● Surge in missing values
● Surge in duplicates
● Poor performance on new
categories
● Poor performance on new
customer segments
● Poor performance on outliers
● Data quality issues affect
accuracy
● Production data doesn’t match
test/training
● Accuracy is decaying over time
● Data drift in inputs
● Concept drift in outputs
● Extreme predictions for out of
distribution data
● Model not generalizing on new
data / new segments
● Major customer behavior shift
5

6. Data Logs
Model Metadata
Pipeline Metadata
i.e. data profiling
Data profiling refers to the analysis of information [...] in order to clarify the
structure, content, relationships, and derivation rules of the data [Wikipedia]
6
Data monitoring starts with logging

7. 7
Sampling Profiling
Pros
● Easy to build
● Little upfront design
● Log & raw data analysis identical
● Scalable & lightweight
● Flexible & configurable
● Rare events and outlier-dependent metrics
● Directly interpretable results
Cons
● I/O & storage
● Noisy
● Requires statistical analysis
● Rare events & outliers
● Min/max, unique values, etc
● Data dependent output format
● No existing widespread solutions
● Mathematical & engineering challenges
Data logs: sampling vs. profiling

8. 8
Data logs: must be accurate
Median: errors in the estimate of the median for sampling vs profiling for various distributions. Mean
absolute error and mean relative (fractional) absolute error are shown.

9. 9
Data logs: must be scalable
Dataset Size # of entries # of features Memory
consumption
Output size
Lending Club 1.6G 2.2M 151 14MB 7.4MB
NYC Tickets 1.9G 10.8 43 14MB 2.3MB
Pain pills 75GB 178M 42 15MB 2MB

10. 10
Logging ML data at scale
Four key paradigms:
● Approximations rather than exact results
● Lightweight
● Additive
● Batch and streaming support
profile: collection of lightweight metrics that provide these
properties

11. Lightweight
Old Approach
11
process process process process
Data Warehouse/
Data Lake
Processing Engine
New Approach
process
profiling
process
profiling
process
profiling
process
profiling
Profile Store
Analysis
Only feasible if:
● Profiling is fast
● Profiling is not memory intensive

12. Additive
12
dataset 1 dataset 2 dataset 3
sort (shuffle)
reduce step
Median
dataset 1
profile 1
dataset 2
profile 2
dataset 3
profile 3
add(profile1, 2, 3)
Estimated Median

13. Batch and streaming support
13
partition
1
profiling
partition
2
profiling
partition
3
profiling
partition
n
profiling
Spark/Hive
Query Engine
No shuffle!
day 0
profiling
day 1
profiling
day 2
profiling
day 3
profiling
... ...
sum(profiles)

14. Approximate Statistics
● Using Stochastic Streaming Algorithms
○ Model the problem as a stochastic process
○ Apache Datasketches is the open source implementation
● Statistics that we focus on at the moment:
○ Histograms
○ Frequent items
○ Cardinality
14

15. whylogs: The Data Logging Library
● Multi-language support: Python + Java
● Support both data engineering and data science
workflows
● Extensibility: image support. Text, video, audio &
embeddings support to come
● Growing integration list:
15

16. 16
whylogs: Python
● A few lines of code to start logging
● Integrate with popular data science libraries
● Out of the box visualization utilities

17. 17
whylogs in Apache Spark
Data Lake
col1
, col2
, …, coln
partition 1
partition 2
partition k
profile
profile
merge (
profile1
,
profile2
…,
profilek
)
global profile
Schema
Metadata
Sketches
profile
Metrics

18. 18
Simple Spark API

19. 19
pySpark support

20. 20
Catch distribution drift in a few lines of code

21. 21
Scalable monitoring at input feature granularity

22. Monitoring layer for ML applications
22

23. 23
bit.ly/whylogs

24. andy@whylabs.ai
@andy_dng
24
bit.ly/whylogs
Help build the open
standard for data
logging!
Thank you!