The document outlines common DataOps challenges encountered when deploying machine learning models in production, including issues with data accessibility, completeness, and compliance. It emphasizes the importance of creating self-service solutions to address these challenges, such as metadata catalogs and orchestration services, in order to improve time-to-insight and governance. The document concludes with a call to action for organizations to assess and enhance their DataOps processes through self-service metrics and design patterns.

1. 1
Overcoming
DataOps hurdles for
ML in Production
August 2020
SANDEEP UTTAMCHANDANI
CHIEF DATA OFFICER and VP OF ENGINEERING
sandeep@unraveldata.com

2. 2
Behind the scenes of a ML Model in Production

3. 3
DATA ML Model in
Production
Discover Prep Build Operationalize
DataOps

4. 4
Top 10 DataOps Battlescars

5. Levels of
Automation
Gather technical metadata
Gather operational metadata
Aggregate tribal
knowledge
1. “I thought the attribute means something else”
Battlescar:
Incorrect assumptions about the meaning of attributes, whether it is the
source of truth, owner/common users, versioning, whether dataset is
trustworthy?
Metric:
Time to
Interpret
Building a Self-Service Metadata Catalog

6. 1. “I thought the attribute means something else?”
Battlescar:
Incorrect assumptions about the meaning of attributes, whether it is the
source of truth, owner/common users, versioning, whether dataset is
trustworthy?
Metric:
Time to
Interpret
Building a Self-Service Metadata Catalog
Intuit

7. 7
2. “Where is the dataset I need for my model?”
Battlescar:
Building a customer support forecasting model. Data was silo’ed across
business units. 4+ months of connecting to data stewards to locate the data
attributes required for building the model
Building a Self-Service Search Service
Levels of
Automation
Indexing of datasets &
artifacts
Search Relevance ranking
Access control of
search results
Metric:
Time to
Find

8. 8
Battlescar:
Building a customer support forecasting model. Data was silo’ed across
business units. 4+ months of connecting to data stewards to locate the data
attributes required for building the model
Building a Self-Service Search Service
Metric:
Time to
Find
2. “Where is the dataset I need for my model?”

9. 9
3. “1000 rows in source database -- why only 50 rows in
data lake?”
Battlescar:
Issues in correctness, completeness, timeliness in moving data
daily/hourly from transactional datastores to centralized data lake
Metric:
Time to
Move
Building a Self-Service Data Movement service
Data Ingestion Configuration
Data Transformation
Change Mgt
Levels of
Automation

10. 10
4. “Job completed but dashboard graphs have data missing?”
Battlescar:
Jobs are orchestrated using schedulers (such as Airflow, Oozie). Several
times, the job dependencies are incorrect, leading to reporting or model
training jobs to be triggered prematurely.
Metric:
Time to
Orchestrate
Building a Self-Service orchestration Service
Levels of
Automation
Defining Job Dependencies
Robust Job Execution
Production
Monitoring

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5. “Data processing was supposed to complete at 8 am. Its 4pm
and my model retraining job is still waiting?”
Battlescar:
Writing efficient Big Data processing applications is non-trivial. With
plethora of technologies, gaining broad expertise is difficult even for
expert data engineers.
Metric:
Time to
Optimize
Building a Self-Service query optimization Service
Levels of
Automation
Aggregating query, cluster,
resource Stats
Analyzing & correlating
stats
Tuning Jobs

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6. “Customer changed preference to no marketing emails. Why are
we still including in email campaigns?”
Battlescar:
Without a consistent primary key to identify the customer across data
silos, where recurring issues arise. Emerging Data Rights such as
GDPR, CCPA, require complying with customer preferences on what
data is collected, how it is used, deleted on request.
Metric:
Time to
Comply
Building a Self-Service data rights governance Service
Levels of
Automation
Tracking customer data lifecycle
and preferences
Executing customer’s
data rights requests
Use-case
based access
control

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7. “Job pipeline ran for 15 hours and now we detect data
quality issue upon completion -- could we be proactive?”
Battlescar:
Data issues in a long running business critical job leads to missing
insights. Only when results don't look correct that we realize there is an
issue.
Metric:
Time to
Insights
Quality
Building a Self-Service data observability Service
Levels of
Automation
Verify accuracy of data
Detect anomalies
Avoid data
quality issues

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8. “Using the best polyglot datastores -- how do I now write
queries effectively across this data?”
Battlescar:
Significant time spent in planning, design, and writing queries that
process data across datastores
Metric:
Time to
Virtualize
Datastores
Building a Self-Service data virtualization Service
Levels of
Automation
Automatic query routing
Managing datastore
specific queries
Joining across
transactional
sources

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9. “I ran a A/B experiment -- need to build time-consuming
data pipelines to now analyze the data”
Battlescar:
Analyzing experimental results in a consistent fashion is a nightmare. No
consistent definitions between metrics used for experimental analysis
and business reporting
Metric:
Time to A/B
Test
Building a Self-Service A/B Testing Service
Levels of
Automation

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10. “Data processing jobs last week cost us 30% more. Why?”
Battlescar:
Especially in the cloud, $ cost is linear to usage. Tracking budgets and
spend to effectively optimize requires non-trivial effort.
Metric:
Time to
Cost
Governance
Building a Self-Service cost governance Service
Levels of
Automation
Expenditure Observability
Matching
Supply-Demand
Continuous Cost
Optimization

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Wrap up: Advice on Managing your DataOps

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People
Process Technology
DataOps hurdles vary and depends on...

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Self-Service has levels (not binary)

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Discover Prep Build Operationalize
TIME-TO-INSIGHT
Measuring Current DataOps:
Time-to-Insight Metric
DATA

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Discover Prep Build Operationalize
Time-to-Insight Scorecard

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Discover Prep Build Operationalize
Creating Your Time-to-Insight Scorecard
WeeksDaysHoursLegend:

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Call for Action: Making DataOps Self-Service
1. Measure
Create your
Time-to-Insight Scorecard
Self-Service
DataOps
2. Learn
Shortlist 1-2 scorecard
metrics to improve level
of automation
3. Build
Implement well-known
design patterns in your
data platform to make the
metrics self-service

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Upcoming Book: The Self-Service Data Roadmap
Available Sept’20
Early Release Available on O’Reilly:
https://www.oreilly.com/library/view/the-self-service-data/9781492075240/

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CONTACT US TO SCHEDULE A DATA OPERATIONS HEALTH CHECK TODAY
hello@unraveldata.com