Innovating with Machine Learning on AWS - Travel & Hospitality (November 2018)
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Talk @ Duetto Revenue Strategy Forum, 05/11/2018, London.
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Innovating with Machine Learning on AWS - Travel & Hospitality (November 2018)
- 1. Julien Simon Principal Evangelist, Artificial Intelligence & Machine Learning @julsimon Innovating with Machine Learning on AWS November 2018
- 2. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. AWS Global Infrastructure
- 3. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. SelectedTravel & Hospitality Customers https://aws.amazon.com/solutions/case-studies/
- 4. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon.com,1995
- 6. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
- 7. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
- 8. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
- 9. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
- 10. Machine Learning at Amazon.com R E TA I L Demand Forecasting Vendor Lead Time Prediction Pricing Packaging Substitute Prediction C U S TO M E R S Recommendation Product Search Product Ads Shopping Advice Customer Problem Detection S E L L E R S Fraud Detection Predictive Help Seller Search & Crawling C ATA LO G U E Browse-Node Classification Meta-data Validation Review Analysis Product Matching T E X T In-Book Search Named-entity Extraction Summarization/X-ray Plagiarism Detection I M A G E S Visual Search Product Image Enhancement Brand Tracking
- 11. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Put Machine Learning in the hands of every developer and data scientist Our mission
- 12. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Application Services Platform Services Frameworks & Infrastructure API-driven services:Vision, Language & Speech Services, Chatbots AWS Machine Learning Stack Deploy Machine Learning models with high-performance algorithms, broad framework support, and one-click training, tuning, and inference. Develop sophisticated models with any framework, Create clusters for large scale training, Run prediction on trained models.
- 13. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. More ML is built on AWS than anywhere else
- 14. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. More ML is built on AWS than anywhere else
- 16. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
- 17. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon Rekognition Deep Learning-based image analysis service
- 18. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Object & Scene Detection
- 19. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. FaceAnalysis
- 20. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Face Search
- 21. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Explicit Nudity Nudity Graphic Male Nudity Graphic Female Nudity Sexual Activity Partial Nudity Suggestive Female Swimwear or Underwear Male Swimwear or Underwear Revealing Clothes Image Moderation
- 22. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Celebrity Recognition
- 23. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Text in Image
- 24. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon Rekognition Video Deep Learning-based video analysis service
- 25. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
- 26. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Sky News used Amazon Rekognition to perform real- time identification of guests as they entered St. George’s Chapel
- 27. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon Polly Text-to-speech service
- 28. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon Polly “Salut, je m’appelle Léa. Je suis la nouvelle voix française de Polly.” Amazon Polly:Text In, Life-like SpeechOut 27 languages, 54 voices
- 29. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Real-life speech with SSML <?xml version="1.0" encoding="ISO-8859-1"?> <speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis" xml:lang="en-UK"> <amazon:auto-breaths> Your reservation for <say-as interpret-as="cardinal">2</say-as> rooms on the <say-as interpret-as="ordinal">4th</say-as> floor of the hotel on <say-as interpret-as="date" format="mdy">3/21/2019</say-as>, with early arrival at <say-as interpret-as="time" format="hms12">12:35pm </say-as> has been confirmed. Please call <say-as interpret- as="telephone">(888) 555-1212</say-as> with any questions. </amazon:auto-breaths> </speak>
- 30. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Duolingo Duolingo is the most popular language- learning platform and the most downloaded education app in the world, with more than 170 million users. They have run six A/B tests, testing an Amazon Polly voice against a voice from otherTTS providers. For all of these experiments, the winning condition was theAmazon Polly voice https://aws.amazon.com/blogs/machine- learning/powering-language-learning-on- duolingo-with-amazon-polly/
- 31. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon Translate Neural Machine Translation Service
- 32. 137 Language Pairs • English • Spanish • Portuguese • German • French • Arabic • Simplified Chinese • Japanese • Russian • Italian • Traditional Chinese • Turkish • Czech .Coming soon : Danish, Dutch, Finnish, Hebrew, Polish, and Swedish
- 33. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Hotels.com « At Hotels.com, we are committed to offering all of our customers the most relevant and up to date information about their destination.To achieve that, we operate 90 localized websites in 41 languages.We have more than 25M customer reviews and more are coming in every day, making a great candidate for machine translation. Having evaluated AmazonTranslate and several other solutions, we believe that AmazonTranslate presents a quick, efficient and most importantly, accurate solution » Matt Fryer,VP and Chief Data Science Officer, Hotels.com
- 34. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon Transcribe Speech-to-Text service
- 35. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Support for telephony audio Timestamp generation Intelligent punctuation and formatting Recognize multiple speakers Custom vocabulary English & Spanish Automatic speech recognition service
- 36. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon Comprehend Natural Language Processing
- 37. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Fully managed natural language processing Discover valuable insights from text Entities Key Phrases Language Sentiment Amazon Comprehend English, Spanish, Portuguese, German, Italian, French
- 38. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Entity Extraction
- 39. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon Lex Conversational Interfaces
- 40. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Intents A particular goal that the user wants to achieve Utterances Spoken or typed phrases that invoke your intent Slots Data the user must provide to fulfill the intent Prompts Questions that ask the user to input data Fulfillment The business logic required to fulfill the user’s intent BookHotel
- 41. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. RedAwning “Using Amazon Connect with Amazon Lex, it was easy to build an intelligent virtual agent to answer calls, match guests with their reservations, and engage naturally with users. Scarlett can resolve the issues that guests most frequently call about, which allows us to easily scale our operations.” Tim Choate, Founder & CEO https://aws.amazon.com/solutions/case-studies/red-awning/
- 43. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. ML is still too complicated for everyday developers Collect and prepare training data Choose and optimize your ML algorithm Set up and manage environments for training Train and tune model (trial and error) Deploy model in production Scale and manage the production environment
- 44. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon SageMaker Pre-built notebooks for common problems K-MeansClustering Principal Component Analysis Neural TopicModelling FactorizationMachines Linear Learner XGBoost Latent Dirichlet Allocation ImageClassification Seq2Seq, And more! ALGORITHMS Apache MXNet, Chainer TensorFlow, PyTorch Caffe2, CNTK, Torch FRAMEWORKS Set up and manage environments for training Train and tune model (trial and error) Deploy model in production Scale and manage the production environment Built-in, high- performance algorithms Build Easily build, train, and deploy Machine Learning models
- 45. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon SageMaker Pre-built notebooks for common problems Built-in, high- performance algorithms One-click training Automatic Model Tuning Build Train Deploy model in production Scale and manage the production environment
- 46. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon SageMaker Fully managed hosting with auto- scaling One-click deployment Pre-built notebooks for common problems Built-in, high- performance algorithms One-click training Automatic Model Tuning Build Train Deploy
- 47. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Selected Amazon SageMaker customers
- 49. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Expedia • Expedia have over 10 million images from 300,000 hotels • Using great images boosts conversion. • Using Keras and GPU instances, they fine-tuned a pre-trained Convolutional Neural Network using 100,000 images • Hotel descriptions now automatically feature the best available images https://news.developer.nvidia.com/expedia-ranking- hotel-images-with-deep-learning/
- 50. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Trainline • Trainline is the UK's leading independent train ticket retailer. • Using Amazon DynamoDB and Spark on Amazon EMR, they built models to predict ticket price evolution over time. • Customers save 49% on average. https://www.thetrainline.com/price-prediction
- 51. CRM RMSROOM RATES ML GUEST RESERVATION RESERVATION Jessica Yu CHECK-IN: 11/7/2018 CHECK-OUT: 11/10/2018 ROOM TYPE: QUEEN ROOM RATE: $99 TARGETED UPGRADES
- 52. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. FRAMEWORKS AND INTERFACES PLATFORM SERVICES APPLICATION SERVICES Amazon Rekognition Amazon Polly Amazon Lex Democratization of ML Amazon Rekognition Video Amazon Transcribe Amazon Comprehend Amazon SageMaker AWS DeepLens Deep Learning AMI Amazon Translate
- 53. Julien Simon Principal Evangelist, Artificial Intelligence & Machine Learning @julsimon https://ml.aws https://aws.amazon.com/blogs/machine-learning https://medium.com/@julsimon https://youtube.com/juliensimonfr
Notes de l'éditeur
- 18 Regions, 55 Azs 5 Regions coming: Bahrain, Cape Town, Hong Kong, Stockholm, and a second GovCloud Region in the US.
- Helping recommend what might interest you, by learning from other customers who have purchased this item have also liked.
- Amazon Echo is a hands-free speaker you control with your voice. Echo connects to the Alexa Voice Service to play music, make calls, send and receive messages, provide information, news, sports scores, weather, and more—instantly. All you have to do is ask.
- Amazon Robotics was founded in 2003 on the notion that in order to meet consumer demands in eCommerce, a better approach to order fulfillment solutions was necessary. Amazon Robotics empowers a smarter, faster, more consistent customer experience through automation automates fulfilment center operations using various methods of robotic technology including autonomous mobile robots, sophisticated control software, language perception, power management, computer vision, depth sensing, machine learning, object recognition, and semantic understanding of commands.
- Amazon Prime Air is a service that will deliver packages up to 2.5 kg in 30 minutes or less using small drones and relies extensively on visual object recognition. We have Prime Air development centers in the United States, the United Kingdom, Austria, France and Israel.
- Amazon Go is a new kind of store with no checkout required. We created the world’s most advanced shopping technology so you never have to wait in line. With our Just Walk Out Shopping experience, simply use the Amazon Go app to enter the store, take the products you want, and go! No lines, no checkout. (No, seriously.) No lines, no checkout Our checkout-free shopping experience is made possible by the same types of technologies used in self-driving cars: computer vision, sensor fusion, and deep learning. Our Just Walk Out Technology automatically detects when products are taken from or returned to the shelves and keeps track of them in a virtual cart. When you’re done shopping, you can just leave the store. Shortly after, we’ll charge your Amazon account and send you a receipt.
- This is just a sample of the range of AI-related services that we use across Amazon.com to help build better experiences for our customers. Mandy of which you don’t ever *SEE* as a customer. Our order fulfillment services, how we pack our trucks, and all of the logistics from the time you place your order until it shows up on your doorstep is completely directed by our AI advancements.
- Up to 100 faces
- Recognizing clients
- User Generated Content You can use the ‘MinConfidence’ parameter in your API requests to balance detection of content (recall) vs the accuracy of detection (precision).
- You can use the ‘MinConfidence’ parameter in your API requests to balance detection of content (recall) vs the accuracy of detection (precision).
- Polly also support Speech Synthesis Markup Language (SSML) Version 1.0 The Voice Browser Working Group has sought to develop standards to enable access to the Web using spoken interaction.
- …Amazon Comprehend, a Natural Language Processing service that enables customers to discover insights from text. 1/ Without provisioning a server, Comprehend can understand documents, social network posts, articles, and any other data in AWS 2/ Simply provide text stored in data lake in S3 via Comprehend API, and Comprehend uses NLP to give you highly accurate info about what it contains in 4 categories: a/ entities (people, places, dates, brands, qtys) b/ key phrases that provide significance to the text c/ language being used d/ sentiment
- First, you need to collect and prepare your training data to discover which elements of your data set are important. Then, you need to select which algorithm and framework you’ll use. After deciding on your approach, you need to teach the model how to make predictions by training, which requires a lot of compute. Then, you need to tune the model so it delivers the best possible predictions, which is often a tedious and manual effort. After you’ve developed a fully trained model, you need to integrate the model with your application and deploy this application on infrastructure that will scale. All of this takes a lot of specialized expertise, access to large amounts of compute and storage, and a lot of time to experiment and optimize every part of the process. In the end, it's not a surprise that the whole thing feels out of reach for most developers.
- SageMaker makes it easy to build ML models and get them ready for training by providing everything you need to quickly connect to your training data, and to select and optimize the best algorithm and framework for your application. Amazon SageMaker includes hosted Jupyter notebooks that make it is easy to explore and visualize your training data stored in Amazon S3. You can connect directly to data in S3, or use AWS Glue to move data from Amazon RDS, Amazon DynamoDB, and Amazon Redshift into S3 for analysis in your notebook. To help you select your algorithm, Amazon SageMaker includes the 10 most common machine learning algorithms which have been pre-installed and optimized to deliver up to 10 times the performance you’ll find running these algorithms anywhere else. Amazon SageMaker also comes pre-configured to run TensorFlow and Apache MXNet, two of the most popular open source frameworks, or you have the option of using your own framework.
- You can begin training your model with a single click in the Amazon SageMaker console. The service manages all of the underlying infrastructure for you and can easily scale to train models at petabyte scale. To make the training process even faster and easier, Amazon SageMaker can automatically tune your model to achieve the highest possible accuracy.
- Once your model is trained and tuned, SageMaker makes it easy to deploy in production so you can start generating predictions on new data (a process called inference). Amazon SageMaker deploys your model on an auto-scaling cluster of Amazon EC2 instances that are spread across multiple availability zones to deliver both high performance and high availability. It also includes built-in A/B testing capabilities to help you test your model and experiment with different versions to achieve the best results. For maximum versatility, we designed Amazon SageMaker in three modules – Build, Train, and Deploy – that can be used together or independently as part of any existing ML workflow you might already have in place.
- Assume a guest, Jessica Yu, already has a reservation. Prior to her arrival, she gets a pre-arrival notification with opportunities for her to upgrade her room and/ or select amenities she might like. The data on her reservation and her broader profile info is in the CRM – Revinate in this case. Room rates come from Duetto, the Revenue Management System. This integration is already live but one place where in the future it can become even more powerful is through targeted upgrades. Leveraging machine learning, we can predict which room upgrades and which amenities are most likely to resonate with her. This makes life better for her because she doesn’t have to sort through what at some fancier hotels and resorts might be dozens of options. And it’s also great for the hotel because revenue is optimized through both higher conversion (based on showing Jessica the right thing) and better rate (dynamic based on season, availability, and many other possible factors).
- SageMaker is going to make it much easier for everyday developers to build machine-learning models. But, people and developers are still really interested in learning more about how they can use machine learning. They want to do it, so they're reading all kinds of literature, and there are some code samples they can play around with. But, for any of us who've had to learn something new that has any kind of complexity, there's no substitute for hands-on training and application. And so we thought about: What can we do that would allow our builders and our developers to get this hands-on training? Our teams worked on this problem and developed AWS DeepLens, which is the world's first wireless deep-learning-enabled video-camera for developers.
- AWS DeepLens is a high-definition camera with on-board compute that is optimized for deep learning. It comes with computer-vision models that we've already built that you can use right on the camera, or you can build your own in SageMaker and import them over the air via the console with a few clicks to DeepLens. It has Greengrass in it. So in addition to writing the models, you can program Greengrass to run various Lambda triggers. There's lots of tutorials and prebuilt models for you, so you can get started right away. In fact, we believe that you'll be able to get started running your first deep-learning computer-vision model in 10 minutes from the time that you unbox the camera. You can provide and you program this thing to do almost anything you can imagine. So for instance, you could imagine programming the camera with computer-vision models where, if you recognize a license plate coming into your driveway, it will open the garage door. Or you could program it to send you an alert when your dog gets on the couch. Really, you can do almost anything. And it's going to give you an opportunity to get learning very quickly in a way that you haven't been able to do before.
- EACH OF THESE ARE AVAILBLE - TODAY WE WILL DIVE INTO OBJECT DETECTION AS A WAY TO GET YOU STARTED… AFTER THIS WORKSHOP YOU THEN EXPLORE THESE OTHER SAMPLES, CREATE CUSTOM FUNCTIONALITY OR START YOUR OWN PROEHCT FROM SCRATCH Learn the basics of machine learning through hands on examples and sample projects sample projects of varying difficulty available for use: object detection, artistic style transfer, face recognition, hot dog Not hot dog, cat vs dog, license plate detection Use existing sample projects or extend the sample project with your own custom functionality (example detect when your dog is sitting on the couch and send an sms) or create your own project Go deeper through integrations with Sage Maker, Greengrass, and other AWS services
- So far, we've discussed the bottom and middle layers of the machine learning stack – first we talked about the frameworks and the deep learning AMI for expert practitioners. Then, SageMaker and DeepLens in the middle layer to bring ML capabilities to all developers. Now, at the top of the stack, we serve developers and companies who want to add solution-oriented intelligence to their applications through an API call rather than developing and training their own models. These are services that exhibit artificial intelligence that emulates a human’s cognitive skills. Last year, we announced three services in this area: Amazon Rekognition (image analysis), Amazon Polly (text-to-speech), and Amazon Lex (conversational applications).