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Genius: Machine Learning at Condè Nast Italy

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Condè Nast Italy creates Genius, the Machine Learning system behind some of the most known brands like Vanity Fair, Vogue, GQ and Wired.
Genius helps these brands to improve the user experience inside their websites.
Starting from the idea and KPIs we take a look at the infrastructure which is a mix of technologies like AWS Rekognition, Kinesis, Sagemaker and Google Cloud Platform Computer Vision APis + Cloud Natural Language.
In the end some points related to the costs and the benefits in having a Serverless approach.

Published in: Engineering
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Genius: Machine Learning at Condè Nast Italy

  1. 1. Serverless Hamburg – 12 March 2018 Marco Viganò Digital CTO Serverless Hamburg 12 March 2018
  2. 2. Serverless Hamburg – 12 March 2018
  3. 3. Serverless Hamburg – 12 March 2018
  4. 4. Serverless Hamburg – 12 March 2018 CN.numbers // by month 30M Unique Visitors 250M Page Views 20% Desktop 80% Mobile 46% SEO 29% Social
  5. 5. Serverless Hamburg – 12 March 2018 CN.technologies
  6. 6. Serverless Hamburg – 12 March 2018 CN.blueprint
  7. 7. Serverless Hamburg – 12 March 2018 CN.challanges[]
  8. 8. Serverless Hamburg – 12 March 2018 CN.challanges[0] === 'images' • More than 10M images in our systems • No automatic galleries / simple queries for editors • Manual tagging: a lot of mistakes 1. Vigano 2. Viganó 3. Vigano’ 4. Vigan 5. Vigantildesup 6. Viganò
  9. 9. Serverless Hamburg – 12 March 2018 CN.challanges[0] === 'images' 1. Shaq 2. Shaquille 3. Diesel 4. The Big Fella 5. Shaquille O’Neal
  10. 10. Serverless Hamburg – 12 March 2018 CN.challanges[1] === 'text'
  11. 11. Serverless Hamburg – 12 March 2018 CN.challanges[2] === 'users' Users behavior in our websites: • Articles readed • Authors followed • Topics of interests • Social Network actions
  12. 12. Serverless Hamburg – 12 March 2018 CN.challanges[3] === 'analysis' and CN.challanges[3] === 'predictions'
  13. 13. Serverless Hamburg – 12 March 2018 CN.ingredients Cognito API Gateway + Lambda functions Kinesis RDS + S3 Rekognition + ML Quicksight Cloud Functions Natural Language API Cloud Vision API GENIUS
  14. 14. Serverless Hamburg – 12 March 2018 CN.genius.call_to_action();
  15. 15. Serverless Hamburg – 12 March 2018 CN.genius.welcome_back();
  16. 16. Serverless Hamburg – 12 March 2018 CN.genius.recommend();
  17. 17. Serverless Hamburg – 12 March 2018 while(true) CN.genius.improvements();
  18. 18. Serverless Hamburg – 12 March 2018 text text text text CN.analize().overview();
  19. 19. Serverless Hamburg – 12 March 2018 I’m a HAPPY user!!! CN.analize().overview().addML(); ML
  20. 20. Serverless Hamburg – 12 March 2018
  21. 21. Serverless Hamburg – 12 March 2018 eval(CN.genius)
  22. 22. Serverless Hamburg – 12 March 2018 CN.genius().user();CN.genius().user().idendity();CN.genius().user().clicktream();CN.genius().suggestedArticles();
  23. 23. Serverless Hamburg – 12 March 2018 CN.genius().images() more than 10M images Automatic celeb gallery Gallery tag driven Tags manager
  24. 24. Serverless Hamburg – 12 March 2018 CN.genius().images() more than 10M images Automatic celeb gallery Gallery tag driven Tags manager
  25. 25. Serverless Hamburg – 12 March 2018 CN.genius().textAnalisys()
  26. 26. Serverless Hamburg – 12 March 2018 demo
  27. 27. Serverless Hamburg – 12 March 2018 • We are not MXNet / Tensorflow experts: we need to speed up • AWS fully managed machine learning service -> serverless • Build, train and deploy ML models easy for our developers • ML arlgorithms on board (Factorization machine, XGBoost, seq2seq…) Sagemaker(CN.Genius);
  28. 28. Serverless Hamburg – 12 March 2018 • Algortithm for forecasting scalar time series • Needs: ADV/Editors best moments to publish a content Sagemaker(CN.Genius).DeepAR(); • Time series input: Unique Visitors and Pageview in a specific date/time for a specific category of our websites • Datasource: We grab this data form Google Analytics
  29. 29. Serverless Hamburg – 12 March 2018
  30. 30. Serverless Hamburg – 12 March 2018 class nextstep extends CN.genius{…} Collaborate filtering with a Graph Database Social interactions (like, comments, tweet/retweet) on a piece of content Data from videos: Rekogniton for videos and Cloud Video Intelligence
  31. 31. Serverless Hamburg – 12 March 2018 class nextstep extends CN.genius{…} Stylist detection { “dress”: “Giorgio Armani”, “bag”: “Gucci” } E-commerce: click to shop
  32. 32. Serverless Hamburg – 12 March 2018 goto costs;
  33. 33. Serverless Hamburg – 12 March 2018 2013/2014 >150 servers! 30 Databases 2015: ROI!!!! 2016 Change Mindset: Thinking Serverless - Photovogue - Starting reducing costs From an angry CFO… to a happy CFO :) 2017 Infrastucture improvements 50 servers - 8 Databases Costs = on premise / 4 On premise 2018 Continuos improvements: Serverless *.* Docker / K8 / ECS
  34. 34. Serverless Hamburg – 12 March 2018 Thank You Marco Viganò @Sasha0423

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