The document discusses the significance of DataOps in enhancing data efficiency and impact within organizations. It highlights the challenges faced by data scientists and web developers, emphasizing the need for a comprehensive data pipeline and testing process to ensure quality and reliability in machine learning model development. Additionally, it addresses the importance of data versioning and automated processes to improve governance and collaboration in data management.

1. DataOps: The Key to
Exponential Data Impact
Luis Vaquero

2. 87%
https://venturebeat.com/2019/07/19/why-do-87-of-data-science-projects-never-make-it-into-production/

4. Luis Vaquero
Director of Data at Goco
Reader in Computer Science at Univ of Bristol
Dyson, HPE, HP Labs, Telefonica/O2

6. “20% of my time building my
model,
and 80% of my time
cleaning data”
Every Data Scientist in 2020

7. “20% of my time building my
app,
and 80% of my time
fighting the browser/the
infra/Spring config...”
Every Web developer in 2000

8. https://towardsdatascience.com/the-rise-of-dataops-from-the-ashes-of-data-governance-da3e0c3ac2c4

9. Pipeline Compiler
“Data pipelines are simply
compilers that use data as the
source code.”
Ryan Gross

10. Each Data Team Write their Own “Compiler”
https://towardsdatascience.com/the-rise-of-dataops-from-the-ashes-of-data-governance-da3e0c3ac2c4
data quality, metadata, raw or slightly
modelled data
Lexical and Syntactic
Analysis

11. Each Data Team Write their Own “Compiler”
https://towardsdatascience.com/the-rise-of-dataops-from-the-ashes-of-data-governance-da3e0c3ac2c4
Lexical and Syntactic
Analysis
Semantic Analysis
business understanding, model creation and
experiments
data quality, metadata, raw or slightly
modelled data

12. Each Data Team Write their Own “Compiler”
https://towardsdatascience.com/the-rise-of-dataops-from-the-ashes-of-data-governance-da3e0c3ac2c4
Lexical and Syntactic
Analysis
Semantic Analysis
Code Generation
business understanding, model creation and
experiments
data quality, metadata, raw or slightly
modelled data

13. Each Data Team Write their Own “Compiler”
https://towardsdatascience.com/the-rise-of-dataops-from-the-ashes-of-data-governance-da3e0c3ac2c4
further tests, robustness
Lexical and Syntactic
Analysis
Semantic Analysis
Code Generation
Optimisation
business understanding, model creation and
experiments
data quality, metadata, raw or slightly
modelled data

14. The Data Pipeline “Compiler”

15. ML
Feature
Extraction
Analysis Tools
http://cidrdb.org/cidr2020/papers/p8-agrawal-cidr20.pdf

16. http://cidrdb.org/cidr2020/papers/p8-agrawal-cidr20.pdf
Configuration
Resource
Mgmn &
Monitoring
Model
Monitoring
Serving
Infrastructure

17. http://cidrdb.org/cidr2020/papers/p8-agrawal-cidr20.pdf
Data
Verification
Process Management
Tools
Model
Monitoring

18. http://cidrdb.org/cidr2020/papers/p8-agrawal-cidr20.pdf
?

19. http://cidrdb.org/cidr2020/papers/p8-agrawal-cidr20.pdf
Providing complete and usable third-party solutions is
non-trivial

20. https://www.eecs.tufts.edu/~dsculley/papers/ml_test_score.pdf

21. Where do we test here?
Pipeline
ML
Model
Model
Output
Raw
Data
Training/
serving
Data

22. DataOps
Tests
Raw
Data
Pipeline
ML
Model
Model
Output
Training/
serving
Data

23. DataOps
Tests
Raw
Data
Pipeline
ML
Model
Model
Output
Training/
serving
Data
Data
Validator

24. What Kind of Tests?
1. data validator: schema conformance and evolution
a. also a way to document new features used in the pipeline
b. Also about trends and anomalies
https://blog.acolyer.org/2019/06/05/data-validation-for-machine-learning/

25. DataOps
Tests
Raw
Data
Pipeline
ML
Model
Model
Output
Training/
serving
Data
Data
Validator
Data
Analyser

26. What Kind of Tests?
1. data validator: schema conformance and evolution
a. also a way to document new features used in the pipeline
2. data analyser: basic statistics
a. Bias
b. Feature/distribution skew
c. ...
https://blog.acolyer.org/2019/06/05/data-validation-for-machine-learning/

27. New Feature Engineering:
“Instead of deriving the math
before feeding the model, we
ensure our features comply
with certain properties so that
the NN can do the math
effectively by itself” -- Airbnb
https://arxiv.org/pdf/1810.09591.pdf

28. DataOps
Tests
Raw
Data
Pipeline
ML
Model
Model
Output
Model
Unit
Tester
Training/
serving
Data
Data
Validator
Data
Analyser

29. What Kind of Tests?
1. data validator: schema conformance and evolution
a. also a way to document new features used in the pipeline
2. data analyser: basic statistics
a. Bias
b. Feature/distribution skew
c. ...
3. model unit tester looks for errors in the training code using
synthetic data (schema-led fuzzing)
https://blog.acolyer.org/2019/06/05/data-validation-for-machine-learning/

30. DataOps
Tests
Raw
Data
Pipeline
ML
Model
Model
Output
Data
Validator
Data
Analyser
Model
Unit
Tester
Training/
serving
Data
Model
Analyser

31. Model Analysis
https://arxiv.org/pdf/1810.09591.pdf
“Smooth distributions
coming from the lower
layers ensure that the upper
layers can correctly
‘interpolate’ the behavior for
unseen values…. ensure the
input features have a smooth
distribution” -- Airbnb

32. DataOps
Tests Monitoring
Raw
Data
Pipeline
ML
Model
Model
Output
Data
Validator
Data
Analyser
Model
Unit
Tester
Training/
serving
Data

33. What Kind of Tests?
1. data validator: schema conformance and evolution
a. also a way to document new features used in the pipeline
2. data analyser: basic statistics
a. Bias
b. Feature/distribution skew
c. ...
3. model unit tester looks for errors in the training code using
synthetic data (schema-led fuzzing)
4. monitoring tests check the output of the model to trigger alerts
https://blog.acolyer.org/2019/06/05/data-validation-for-machine-learning/

34. Monitoring
Model performance
dashboard
1. model output metrics
through training, validation,
testing, and deployment.
2. data input metrics
3. operational telemetry
Image from: https://www.parallelm.com/

35. https://eng.uber.com/backtesting-at-scale/

36. Pipeline Compiler
Data Version
Control

38. What is Special about ML?
1. New Artefacts to Manage
a. Data
b. Metadata: Hyperparameters
c. Code: architecture
d. Model: executable software “built from the data”
e. Experiment: Data + metadata + Hyperparams + Code -> Model
2. Different Process
a. Trial and error: Scientific method
b. Reproducibility - traceability
c. Explainability

39. https://medium.com/thelaunchpad/retracing-your-steps-in-machine-learning-ml-versioning-74d19a66bd08
Using Excel to Track your versions of data, metadata,
hyperparameters, and architecture?

41. The Data Version Control Tipping Point
● Datasets can be versioned, branched, acted upon by versioned
code to create new data sets
● Test and fill bugs against data
● Enable quality control for compiler steps
● Automated lineage and schema change deceting
● Make guarantees about system components

42. https://towardsdatascience.com/the-rise-of-dataops-from-the-ashes-of-data-governance-da3e0c3ac2c4
Pipeline
Compiler
Data Version
Control

43. https://towardsdatascience.com/the-rise-of-dataops-from-the-ashes-of-data-governance-da3e0c3ac2c4
Pipeline
Compiler
Data Version
Control
Process & Org
Changes

46. Tools
Relationships
Processes
https://blog.dominodatalab.com/introducing-the-data-science-maturity-model/

47. Ad Hoc Exploration1
Tools RelationshipsProcesses
- Isolated efforts
- No repeatability
- Siloed data
- Local dev
- No business buy-in - transactional
- Ivory tower
Tools
Relationships
Processes

48. Reproducible, but limited2
Tools RelationshipsProcesses
- Repeatability is patchy
- Poor governance
- Shy centralisation
- Static reports
- Heavy transactional - rapport
- Team management support only
Tools
Relationships
Processes

49. Defined, Controlled3
Tools RelationshipsProcesses
- Formal but manually enforced- Good centralization
(metadata, access)
- Live retrospective
reports
- Incipient experimentation
- Empathy
Tools
Relationships
Processes

50. Automated4
Tools RelationshipsProcesses
- Automated and searchable
- Broad accessibility
- Best practice
- Event-driven
- Wide experimentation
- Analytics is the business
Tools
Relationships
Processes

54. https://dl.acm.org/citation.cfm
?id=3098021
https://engineering.fb.com/core-
data/introducing-fblearner-flow-
facebook-s-ai-backbone/