Solve complex business problems with Amazon Personalize and Amazon Forecast (April 2019)
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Solve complex business problems with Amazon Personalize and Amazon Forecast (April 2019)
- 1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Solve complex business problems with Amazon Forecast andAmazon Personalize Julien Simon Global Evangelist,AI & Machine Learning @julsimon
- 2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 3. Common applications& use cases Personalized recommendations Search reranking Notifications and emailsRelated Items
- 4. 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
- 5. 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
- 6. 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
- 7. DeepLearningdeliversstateof theartperformance 0.954 0.928 0.925 0.922 0.91 0.856 Rolling Average T-SVD [2009] PMF [2008] RRN [2017] DeepRec [2017] HRNN Ratings RMSE on Netflix 98 MM interactions, 500k users, 18k items Rolling Average T-SVD [2009] PMF [2008] RRN [2017] DeepRec [2017] HRNN 0.933 0.916 0.871 0.857 0.846 Rolling Average FM [2012] I-AutoRec [2015] RNN HRNN Ratings RMSE on MovieLens 20 MM interactions, 173k users, 131k items Rolling Average FM [2012] I-AutoRec [2015] RNN
- 8. AmazonPersonalize Improvecustomerexperienceswithpersonalizationandrecommendations K E Y F E A T U R E S Context-aware Recommendations Automated machine learning Continuous learning to improve performance
- 10. FeedingdatatoAmazonPersonalize 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. Traincustommodelsonceyouingestdata Use AutoML or pick a predefined algorithm recipes AutoML Hyper Parameter Optimization
- 12. Demo
- 13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 14. Sample use cases Product demand Workforce demand Financial metrics Inventory planning
- 15. Accuracy is the most important factor in forecasting Under-forecasting leads to lost opportunity Over-forecasting leads to wasted resources
- 19. Traditionaltime-seriesmodels • Independent forecasts • Strong structural assumptions • De-facto industry standard • Well-understood, > 50 yrs. research • Data must match the structural assumptions • Cannot identify patterns across time series
- 20. Example Data Seasonality Trend Noise ? or useful information?
- 21. Traditional methods struggle with real-world forecasting Can’t handle time-series with no history Only process a single time- series at a time Don’t consider additional inputs: related time-series, metadata Only predict a single value: how trustworthy is it?
- 22. Can we do better?
- 26. Usingadditionalinputs • Additional inputs can • Explain historical data • Drive forecast behavior • Examples from retail • Price information • Information about promotions • Out-of-stock information • Web page views • Known future events • Categorical inputs can be used to identify group-level patterns Fashion Women’s Clothing Shoes Watches Men’s Clothing Shoes Watches Girls' Clothing Shoes Watches Boys' Clothing Shoes Watches
- 30. Probabilisticforecasts • Quantification of uncertainty • Support optimal decision making • Make “wrong” forecasts useful • Forecasts can be obtained for different quantiles of the predictive distribution p10: 10% of predictions with be lower p50: the mean value p90: 90% of predictions with be lower p10-p90 interval: 80% of possible predictions.
- 31. Deeplearningtime-seriesmodels • Global models: identify patterns using all available time series • Group-dependent seasonality and lifecycle • Behavior in response to extra inputs • Weak structural assumptions • Can be significantly more accurate than traditional methods • Can easily incorporate and learn from rich metadata • Support cold-start forecasts for new items
- 33. AmazonForecast Improveforecastingaccuracybyupto50%at1/10ththecost K E Y F E A T U R E S Consider multiple time-series at once Automatic machine learning Visualize forecasts & import results into business apps Evaluate model accuracy Schedule forecasts and model retraining
- 35. AmazonForecast:How itworks Target time-series Item meta-data tegory, genre, brand, etc. Amazon Forecast Related time-series
- 36. Traincustommodelsonceyouingestdata Use AutoML or pick a predefined algorithm recipes AutoML Hyper Parameter Optimization
- 40. Thank you! © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Julien Simon Global Evangelist, AI & Machine Learning @julsimon https://medium.com/@julsimon
Editor's Notes
- This talk is for builders I want to build a new app or website and I want it to work on every platform I want easily leverage AWS from my existing web or mobile apps and I don’t want to rewrite everything I want to learn about cool new development tools like React, GraphQL, CLIs, and serverless technologies I want to focus less on ops and configuration and more on my product If you recognize yourself in one of the previous sentences, you’re in the right room. TODO for the presenter : adjust the banner to the conference you will speak to.
- LAUNCH CUSTOMERS: Domino’s, Navitime, Rbmedia/Recorded Books Inc., Spuul, Zola. One of our launch customers, Domino’s Pizza, is using Amazon Personalize to predict purchasing behavior and apply context about individual customers and their circumstances to deliver personalized promotions and notifications. Spuul, a video streaming platform delivering Indian movies and TV shows to an audience worldwide, was manually categorizing and displaying content to users based on broad segmentation. With Amazon Personalize, they now can provide unique and personalized recommendations to each customer. And Sony Interactive Entertainment (SIE), which is Sony’s video game division overseeing the PlayStation ecosystem, is using Amazon SageMaker and Amazon Personalize to automate and accelerate their machine learning development, and drive more effective personalization at scale. Another launch customer, Zola, develops innovative wedding planning tools to serve couples. They want to provide the best possible recommendations to our customers based on their style, interests, or preferences. Until now, those recommendations were implemented via rule-based ranking, popularity, or, more recently, via a similarity model calculated offline. With Amazon Personalize, Zola can respond to customer actions in real-time and quickly deliver solutions that would have otherwise taken a much larger team and several months development time. NAVITIME, a leading provider of navigation technology and services in Japan, is using Amazon Personalize to to improve the accuracy of predictions in their navigation app by personalizing search results as well as recommended navigation routes, based on the users personal preference.
- 1/ Personalize works very similar to Forecast. You provide data, which in this case is activity stream data from a web application or mobile app, information about the available inventory of things to recommend, and user demographics if known 2/ Personalize loads the data, inspects it, identifies features, selects algorithms and hyperparameters, and then trains and optimizes models 3/ Because Personalize is able to take full advantage of all of the available data to train, the models can be very large, in the gigabytes 4/ We put to use a lot of the lessons we’ve learned over the years to operationalize these sophisticated models to implement a separate feature store for the model data and automatically scale the models themselves to keep latency below 100ms 5/ All of this happens automatically for the user 6/ At the end of the process, users have a customized personalization API that’s been trained on their unique data
- LAUNCH CUSTOMERS: Mercado Libre, CJ Logistics Customers like MercadoLibre, Latin America's most popular e-commerce site. is using Amazon Forecast to predict demand for over 50,000 different products, Forecast’s state-of-the-art deep learning algorithms available out of the box. Forecast is removing all the heavy lifting of setting up pipelines, re-training schedules, and re-generating forecasts, so they can experiment with hundreds of models very easily.
- 1/ To use Amazon Forecast, you provide access to historical data, plus any data which you believe may be useful in making predictions 2/ Amazon Forecast loads the data, inspects it, identifies features, selects algorithms and hyperparameters, and then trains and optimizes models before hosting them in a high availability environment 3/ All of this happens automatically for the user 4/ At the end of the process, users have a customized forecasting API that’s been trained on their unique data 5/ Forecast can forecast any time series scenario, including retail demand, travel demand, AWS usage, revenue, web traffic, and advertising demand
- TODO for the presenter : adjust the banner to the conference you will speak to.