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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Pop-up Loft
Introducing Amazon Personalize (Previ...
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Personalizing user experience is proven to increa...
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Effective personalization requires solving multip...
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Traditional recommender systems aren’t adequate
•...
Deep learning techniques have a direct impact on the bottom
line
SimilarityPopularity
Neural
network
Matrix
factorization
...
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Introducing Amazon Personalize
Real-time personal...
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Common applications & use cases
Personalized
reco...
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Applicable across multiple domains
Amazon Persona...
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Amazon Personalize ‒ Overview
Prepare your data,
...
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Real-time data can be consumed by Amazon Personal...
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Use the Console/API to train and experiment with ...
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Deploy the best models (solutions) by launching a...
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Console to monitor and track all the different st...
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Easy to use console to test personalized recommen...
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Deliver personalized recommendations in productio...
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Science for Personalization
(order and timing mat...
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Science for Personalization
(order and timing mat...
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Amazon Personalize Customers
"At Sony, we are focused on developing predictive models to create an engaging
and relevant experience for all our consume...
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Questions?
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Introducing Amazon Personalize

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Amazon Personalize is a fully-managed service that helps companies deliver personalized experiences, such as recommendations, search results, email campaigns and notifications. It brings over 20 years of experience in personalization from Amazon.com (http://amazon.com/) and puts it in the hands of developers with little or no machine learning experience. Amazon Personalize uses AutoML to automate the entire process of managing and processing data, choosing the right algorithm based on the data, and using the data to train and deploy custom machine learning models — all with a few simple API calls. Join us and learn how you can use Concierge to build engaging experiences that respond to user preferences and behavior in real-time.

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Introducing Amazon Personalize

  1. 1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Pop-up Loft Introducing Amazon Personalize (Preview) Jeet Shangari Sr. Technical Account Manager
  2. 2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Personalizing user experience is proven to increase discoverability, engagement, user satisfaction, and revenue 30% of page views on Amazon are from recommendations … However, most customers find personalization hard to get right
  3. 3. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Effective personalization requires solving multiple hard problems Reacting to user interactions in real time Avoiding mostly showing popular items Handling cold start (insufficient data about new users/items) Scale
  4. 4. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Traditional recommender systems aren’t adequate • Rule-based systems perform poorly, don’t scale and are hard to maintain • Collaborative filtering and matrix factorization methods are good for v1, but deep neural networks, esp. recurrent neural networks, that take into account the sequence of a user’s activity (clicks) out-perform other methods
  5. 5. Deep learning techniques have a direct impact on the bottom line SimilarityPopularity Neural network Matrix factorization +15.4% Engagement Recurrent Neural Net + Bandit Rule-based card ranker Bayesian network model +7.4% Engagement+29% Click Through +20% Click Through
  6. 6. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Introducing Amazon Personalize Real-time personalization and recommendation, based on the machine learning technology used by Amazon.com Generates personalized recommendations for your users via API calls • State-of-the-art deep learning algorithms that out-perform traditional methods • Auto-ML capabilities that automate the entire process from data ingestion to inference • Real-time personalized recommendations – Ability to ingest activity/clickstream data and generate recommendations, in real time, based on the session/context of the use
  7. 7. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Common applications & use cases Personalized recommendations Search reranking Notifications and emailsRelated Items
  8. 8. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Applicable across multiple domains Amazon Personalize can be applied to many domains including • Retail and E-commerce • Video on demand • News • Travel • Personalized notifications
  9. 9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Amazon Personalize ‒ Overview Prepare your data, then upload with the Amazon Personalize API Choose one of our algorithms or tell AutoML to find the best fit Modify your code Retrain continually to improve the model
  10. 10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Real-time data can be consumed by Amazon Personalize Historical user activity User attributes Item catalog Real-time data Mobile SDKs (coming soon) JavaScript SDK Amazon S3 bucket Server-Side SDKs Offline data
  11. 11. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Use the Console/API to train and experiment with models Use AutoML or pick a pre-defined algorithm • Choose a pre-existing algorithm (packaged as Personalize Recipes) or use AutoML and Personalize will pick the right recipe for you • You can train custom deep learning models on your data and compare accuracy metrics with 2 API calls
  12. 12. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Deploy the best models (solutions) by launching a campaign Amazon Personalize Campaigns • Launching a campaign will deploy all the infrastructure needed to create a personalize endpoint • You can use a simple API to getRecommendatons() or getPersonalizedResults()
  13. 13. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Console to monitor and track all the different steps
  14. 14. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Easy to use console to test personalized recommendations
  15. 15. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Deliver personalized recommendations in production Your web server Browser Inference query Recommendations Recommendation request Recommendations Real-time event data
  16. 16. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Science for Personalization (order and timing matters) Fundamental insight The evolution of historical interest and dis-interest is a good indicator of future preferences (application data-specific patterns)
  17. 17. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Science for Personalization (order and timing matters) Fundamental insight The evolution of historical interest and dis-interest is a good indicator of future preferences (application data specific patterns)
  18. 18. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
  19. 19. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
  20. 20. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Amazon Personalize Customers
  21. 21. "At Sony, we are focused on developing predictive models to create an engaging and relevant experience for all our consumers. However, delivering truly personalized offers and tailored content to millions of users across channels and content formats is no simple task – it requires the complex integration of advanced technology, people, and processes. Amazon Personalize eliminates the heavy lifting of building a personalized recommendation system. We can leverage Amazon Personalize's data pipelines and indexing infrastructure, while developing our models using Amazon SageMaker. The combination of Amazon SageMaker and Amazon Personalize enables us to automate and accelerate our machine learning development, and drive more effective personalization at scale." Gabor Melli Senior Director of Machine Learning, Sony
  22. 22. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Questions?

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