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Similar to Machine learning for Netflix recommendations talk at SF Make School
Machine learning for Netflix recommendations talk at SF Make School
- 1. Machine Learning For Recommendations ANetflix Perspective Faisal Siddiqi 9 Feb 2018, Make School, SF @faisalzs
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- 4. ● > 117million members streaming over 125 million hours a day ● > 190 countries ● > 1000 device types ● Log 100B events/day ● 35.2% of peak US downstream traffic* * Source: https://www.sandvine.com/resources/global-internet-phenomena/2016/north-america-and-latin-america.html Netflix Scale
- 5. Netflix’s History withML Recommendations (Predict Movie Ratings) 2008 - 2009
- 6. ML @ NetflixToday Recommendations Search Artwork Personalization Content Promotion Artwork Generation Artwork Enhancement Asset Automation Creative Personalization Content Other Popularity Valuation Physical Production Studio in the Cloud Streaming QoE Content Delivery Programmatic Marketing … and many more!
- 7. Turn on Netflixand the best content for you starts playing automatically Recommendations
- 8. Recommendations ● A fewseconds to find something great to watch… ● Can only show a few titles ● Enjoyment directly impacts customer satisfaction ● Generates over $1B per year of Netflix revenue ● How? Personalize everything
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- 10. Recommendations with Context 2017-12-1015:40:22 2017-12-23 19:32:10 2017-12-24 12:05:53 2017-12-27 22:40:22 2017-12-29 19:39:36 2017-12-30 20:42:13 Context ActionSequence per user ? Time
- 11. Data from Millions of users Training pipelines Models PrecomputeSystem Rankings Online caches AB Test Allocation Systems & Pipelines
- 12. ML and Systems Source:Hidden Technical Debt in Machine Learning Systems
- 13. ExplorationData Models Deploy Training Data Infra Plays Impressions UserActions Store & Serve Adhoc Exploration Infra Notebooks Libraries Frameworks Model Development Infra Orchestration TensorFlow Scikit-learn Hyper Params Production Deployment Infra Continuous Integration Explore/Exploit Logging Feedback
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- 15. Takeaways ● Machine Learningis extensively used for Recommendations as well as broadly across several disciplines ● Every pixel, every device, every contact presents an opportunity for personalization ● Solid Infrastructure and Systems is a pre-req for large-scale ML ● Investing in robust software best practices is just as important for Machine Learning as for traditional software development
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- 17. Conclusion Netflix is hyperdata-driven and a robust experimentation platform facilitates fast and objective decision-making ML innovations allow us to make the Netflix experience highly personalized enabling member joy
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