Personalization using Machine Learning
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Overview of using Machine Learning for various use cases of Personalization including real-time, offline, hybrid
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Personalization using Machine Learning
- 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
- 19. Digital Marketing – Personalization Use Cases
- 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