Overview of using Machine Learning for various use cases of Personalization including real-time, offline, hybrid

2. What is the next frontier for Personalization?
Algorithms, Data, Computing, SW Architecture

3. Command Stations
Personalization deep dive, sw engineering front
Real time, Batch, Hybrid
Recommendations, Offers, Content

4. Personalization Trilogy
Some level of
customization,
segments
One size fits all, no
customization
One on one,
personalized

5. Elementary K-6
Set the Foundation

6. Data Scientist, SW Engineer – Separate domains
Model
Building
Model
Deployment
Data Store
Content
Delivery
Data Capture
Analytics
Data Scientist realm – Exploration, Analysis, Accuracy
Software Engineer realm – Production, Delivery, Scale

7. Continuum – Broad, Deep
Deep – Data Scientist
- Good enough model
- More data, better data
- Feature selection
Broad – SW Engineering
- Basic ML, scale and automation
- Rapid experimentation, fail fast
- Continuous feedback, update

8. Continuum – Broad, Deep
Execution at scale
- Model building
- Deploying
- Pipeline, Single flow
Optimization
- ML algorithms
- Data, more, higher quality
- Features, analysis

9. How much is good enough?
Model
Building
Data Store
Data Capture
Model
Deployment
Serving Predictions,
Recommendations
AB Testing,
Experiments
Start thin, build as you go
Go beyond the prototype
Enough to support the first use case
Not the best model, much better
than random
Time box, x weeks

10. Middle Grades – Growing years
Platform, Framework

12. Offline, Batch Processing Flow
Applications,
Interactions
Data
Store
Api Layer –
Data capture
Other sources of data,
non transactional,
offline
Train
Run
Customer facing
applications
Api Layer – Serving
recommendations,
insights
Periodically
batch model
training
Periodically run
models
Pre-
computed
Content
Feedback

13. Real-time Processing Flow
Applications,
Interactions
Data
Store
Api Layer –
Data capture
Other sources of data,
non transactional,
offline
Train
Run
Customer facing
applications
Api Layer –
Compute on-the-
fly
Periodically
batch model
training
Model
Store, In-
memory
cache
Feedback

14. Graduates and Beyond
Maturity, Architecture

15. Maturity
Model
Building
Model
Deployment
Data Store
Serving Predictions,
Recommendations
Data Capture
AB Testing,
Experiments

16. Architecture Blueprint
Applications
Api (real-time
content)
Api (delivery) Api (events) Api (feedback)
Pre-
computed
cache,
Feature Data
Event Log
RT Analytics
Signal Data
Api (analytics)
Feedback,
behavior
Transaction,
Event
Personalized
Content
In-App
Data
Personalized
Content
Train models periodically
Run
Models
Model Building
Model
Deployment
Re-run new models
periodically
Raw data or
features

17. Incomplete, Inaccurate, Not Exact

18. Beginning the journey..

19. Digital Marketing – Personalization Use Cases

20. Content Personalization, Recommendations

21. Hybrid, Collaborative + Content

22. Recommendations Blueprint, Real-time, Offline

23. In Closing

24. Questions?
Please reach out for any questions, comments. I would
love to hear from you. My contact info:
email: mukul.sood@gmail.com or
LinkedIn: www.linkedin.com/in/muksudny
Thank You!

25. Acknowledgements
Following references were used for this presentation:
• Towards Data Science blog Personalization, ML
• Data Science Lifecycle - https://docs.microsoft.com/en-us/azure/machine-
learning/team-data-science-process/lifecycle
• Pivotal Greenplum, Cloud Foundry ML platform
• How can we design an Intelligent Recommendation Engine
https://uxplanet.org/how-can-we-design-an-intelligent-recommendation-
engine-b9bb1db4d050
• Qcon SanFrancisco 2015 Talk - https://www.slideshare.net/InfoQ/takes-a-
village-to-raise-a-machine-learning-model?qid=a6608306-b305-40f4-
a624-46bd852e79fb&v=&b=&from_search=16