Abstract: In this era of the digital world, all walk of lives is driven (controlled /influenced) by technology. With the maturity of DevOps, Technology priorities have become more pragmatic to serve business outcomes. One of the most noticeable evolutions in this area is “Code”. Yes, that’s right, it’s no more software as code but Configuration as code, Infrastructure as code, Pipeline as code, Design as code and so on… No exaggeration in describing, Everything as Code ! Now it’s time to manage this code, all these artifacts can be treated like software code and follow the same software development lifecycle. Will take this discussion on how to achieve the scale of code quality in this time of open source, Code contribution in a diversified ecosystem, beyond the boundaries of your team/company. Will share framework /best practices of higher code quality and how it helps in enabling a higher quality of life. Key Takeaways: 1. Code as a foundation of everything we do 2. Factors/framework on scaling code quality 3. Startup mindset aka differentiator on products/services being provided.

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Enable Enterprise Agility
through Engineering Mindset
Harsha Vardhan
Sr Director Software Engineering
Optum Global Solutions
The contents in this presentation is prepared by Harsha Vardhan in his personal capacity and to the best of his knowledge. No lia bility
whatsoever for the accuracy and completeness of the above information ishowsoever assumed. The opinions/ contents expressed/
provided in this article are the author'sown and do not reflect the views of the Optum Global Solutions(India) Private Limited
(“company”). Neither company nor the author shall not be held liable for any improper or incorrect use of the information described and/or
contained herein and assumes no responsibility for anyone's use of the information.”

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Agenda
Complexity Thinking
Enterprise Agility
Engineering Mindset
Q&A

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Kuwait: A Desert on Fire © Sebastião Salgado
5 years to 18 months - it’s all about Agility ++

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Agile means…
Thinking
Differently

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Tactical > Strategic > Cultural
Complexity Thinking

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System Thinking Vs Complexity Thinking
My experience of software development projects has typically involved requirements
provided up front – the problem has been identified & analyzed and a potential solution
has been put forward for us to build. The ideal future state has been defined (a la
Systems Thinking).
Past experience (& evidence outside of my experience) has demonstrated that defining
the future state & closing the gap perhaps isn’t the best way to develop & deliver
software. Either the future state keeps changing or the gap (typically) widens as we
discover new information about the project.

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Be contextual …
*ApplyingAgile andDevOpsprinciple at scale by Gary Gruver

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Raise your bar !!

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Engineering Mindset –
Are we contributing enough in coding?

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Everything as Code !

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Technical Adoption

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How are we measuring Tech debt ?

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Celebrate Coding – are we doing enough ?

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Current Culture
“You are only as good as your last failure”
..Restrict
.. Inconsistent
..Govern
..Focusing on the Negative
Future
“Balancing risk and velocity & focus on outcomes”
..increased throughput & flexibility
..consistency
..focus on the right changes
..focus on success & learn from
failure
Turning the corner on Change Culture

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How might we leverage machine learning to
identify changes with the highest potential risk for
negative outcomes?

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ITSM
Datamart
Data Ingestion
Completed Changes
(Scheduled Batch)
Model Building,
Training, Re-Training,
and Validation
Additional insights to
supplement model
Trained Model
ET
Data Lake
New change
request
Enhanced Risk
assessment
Machine Learning
Model API
Service Now API
Planned Changes
(real-time)
Web Service
Dashboard updated daily
result
input
Source code
Data Processing / Transformation
NLP
Solution Architecture

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• Performance metrics, several models, pick the best
• Gradient-BoostedTrees implementation  XGBoost
• Ensemble  combines many “weak” learners to create strong learner
TPR
FPR
Algorithm AUC Precision Recall F1
Logistic Regression 0.825 0.162 0.135 0.147
Decision Tree 0.831 0.192 0.176 0.184
Random Forest 0.854 0.213 0.191 0.201
Gradient Boosted Trees 0.906 0.292 0.269 0.281
Neural Network - LSTM 0.848 0.089 0.176 0.142
Test-set performance metrics
Higher risk
Model Training & Selection – The Recipe

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Via Machine Learning, we can drive down our cost per incident from Low
Risk Changes.
Scenario :
• We average about 10 incidents from low risk changes per month. Currently the machine
learning is upgrading 10% (1) of those changes to high or critical Risk. This higher risk
profile brings about more review and planning. This in turn leads to more positive
outcomes.
• Making those assumptions, we estimate that we can save approximately $17,000 per year
month or about $204,000 annually from reduction of P1/2 incidents.
Additional Insights

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Engineering Excellence
Optimize for iteration
speed
Push relentlessly toward
automation
Build the right software
abstractions
Develop a focus on high
code quality withcode
reviews
Maintain a respectful
work environment
Build shared
ownershipof code
Invest in automated
testing
Build a culture of
learning and
continuous
improvement
Hire the best talent
Agile Engineering Practices
Its all about Software Engineering Culture…

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Thank You