The document discusses the implementation and scaling of machine learning (ML) across enterprises through an AI platform, emphasizing the importance of platform thinking and collaboration among data science teams. It outlines the characteristics and structure of an AI platform and provides steps for organizations to enhance their AI capabilities. Key recommendations include obtaining buy-in from leadership, creating a central team for AI infrastructure, and focusing on high-impact initiatives.

1. Webinar Series
Platform Approach to Scaling ML Across the
Enterprise
By Olalekan Elesin

2. Agenda
v Introduction
v Platform Thinking
v AI Platform Thinking
v AI Platform – Product Principles
v Now What?

3. Meet me …
vNigerian 🇳🇬
vRead 📖
vPlay 😎
vFootball (Soccer) ⚽ 🥅

4. Introduction

5. Usually Starts with
Alice, CPO Bob, Data Scientist
This is the
best job ever
We need to build a
recommendation
engine
That’s simple.
Let me get to work
on this
immediately

6. And then …
Alice, CPO Bob, Data Scientist
I don’t think I
can do this
aloneWe need a model
to optimize
conversion
Sounds like a task
I’m up for.
Wait, we just
wrapped up …

7. We hire more …
Alice, CPO Data Science Team
We need a model
to optimize
conversion
We need a model
…
We need to change
the world
…

8. And this happens…
Data Science Team
We need a model
to optimize
conversion
We need a model
…
We need to change
the world
…
…
…
We need a model
…
We are getting worked up. Spending
most of our time doing …

9. And this happens…
Data Science Team
We need a model
to optimize
conversion
We need a model
…
We need to change
the world
…
…
…
We need a model
…
More and more
with AI
I’m sure AI can
help us do this,
that and those

10. Platform Thinking

11. What is a Platform?
A platform is a group of technologies that are used as a
base upon which other applications, processes
or technologies are developed. – Techopedia

12. Platform Thinking
Platform
App 1 App 2 App n
App 1 App 2 App n

13. Platform Thinking
ØRemove friction from product/delivery teams by focusing on high quality,
self-service access to foundational technology.
ØCreating an ecosystem of technology and business capabilities.
ØInvest in a foundation for experimentation to ensure every delivery team
has access tools to make testing new ideas and validating learnings easy.
Source: ThoughtWorks - Art of Platform Thinking

14. AI Platform Thinking

15. AI Platform Thinking
Data Science Team
Engineering
80%
Data Science
20%
A day in the life of a
Data Scientist

16. AI Platform Thinking
Engineering
80%
Data Science
20%
A day in the life of a
Data Scientist
Data Science
80%
Engineering
20%
A day in the life of a
Data Scientist

17. What is an AI Platform?
An AI Platform is an internal product made up collection of
TOOLS, INFRASTRUCTURE, and SERVICES to enable the
development, deployment and management of
AI-driven products at scale

18. What is an AI Platform?
AI Platform
Initiative 1 Initiative 2 Initiative nInitiative 3 Initiative … …
By Olalekan Elesin

19. Scaling Use Cases with AI Platform
Pre
AI Platform
After
AI Platform

20. AI Platform Thinking – Long Tail
High
Priority/Business
Value Initiatives
Low Complexity and Quick win Initiatives
D
ata
Scientists
Machine Learning Enthusiasts
RiskandComplexity

21. AI Platform Thinking
• Increase ML use case
experimentation
• Reduce the Time-to-Market
• Increase the Number of AI Use Cases
• Increase the Number of ML
Practitioners
TimetoMarket(T2M)
No of AI Use Cases
T2M by AI Use Cases

22. AI Platform – Product Principles

23. Before We Begin
Machine Learning
Process
Data Science
Process

24. ML problem
framing
Data
integration
Data
preparation
and cleaning
Data collection
Data
visualization
and analysis
Monitoring and
debugging
Model
deployment
Feature
Engineering**
Model Training
and parameter
tuning
Model
Evaluation
Are
business
goals
met?
Dataaugmentation
Feature
augmentation
Prediction
Business
Problem
The Machine Learning Process
Legend
Data Platform
AI Platform
Human Judgement

25. AI Platform – Product Principles
Legend
People and Culture
Technology Model Training Model Serving
ML Model
ManagementFeature
Management
AutoML
Capabilities
AI Services
End-to-End Machine Learning Workflow
Trainings,SupportandWorkshops
ResearchandDevelopment
Model
Monitoring
Feature
Management
Data Labelling
Service

26. Now What?

27. Next Steps – AI Ready Enterprise
v Get Board and C-Suite Buy-in
v AI Evangelism, Community, and Education
v Create a Central Team with the mission to simplify AI Infrastructure
vMachine Learning Engineers, People Manager and Technical Product Manager
v Define AI Use Cases across Product Teams and Business Functions
v Enable Data Scientists to Focus on High Impact Initiatives
v Get Everyone Doing AI

28. Next Steps – AI Platform Product Management Tips
vFocus on internal customer outcomes

29. Example: Internal Customer Outcomes
AI Platform User Outcomes
Improve the technical knowledge required for product engineers/teams to get started with machine learning on the AI Platform
Minimize the amount of time it takes to test a trained model to production
Minimize the amount of effort required to make use of services that provide simple machine learning tasks
Minimize the effort required to provision model training and model serving infrastructure
Improve the likelihood of accessing the right dataset to create machine learning models
Improve the possibility of understanding the predicted results of trained machine learning models
Improve the likelihood to deploy machine learning workflows with continuous integration and continuous deployment pipelines
Improve the likelihood to monitor how models are performing over time

30. Next Steps – AI Platform Product Management Tips
vFocus on internal customer outcomes
vBuild by use case and generalize
vStrong collaboration with Data Science

31. • Email: elesin.olalekan@gmail.com
• Twitter: @elesinOlalekan
• LinkedIn: https://www.linkedin.com/in/elesinolalekan/
• Xing: https://www.xing.com/profile/Olalekan_Elesin
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