Amazon AI for Every Developer
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Amazon AI services: Polly, Rekognition, Lex Amazon Machine Leaning Apache MXNet
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Amazon AI for Every Developer
- 1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Julien Simon AI Evangelist, EMEA @julsimon Amazon AI for every developer
- 2. A Flywheel For Data More Data Better Analytics
- 3. A Flywheel For Data More Data Better Analytics Better Products
- 4. A Flywheel For Data More Data Better Analytics Better ProductsMore Users
- 5. A Flywheel For Data More Data Better Analytics Better ProductsMore Users Click stream User activity Generated content Purchases Clicks Likes Sensor data
- 6. A Flywheel For Data More Data Better Analytics Better ProductsMore Users Click stream User activity Generated content Purchases Clicks Likes Sensor data Object Storage Databases Data warehouse Streaming analytics BI Hadoop Spark/Presto Elasticsearch
- 7. A Flywheel For Data More Data Better Analytics Better ProductsMore Users Click stream User activity Generated content Purchases Clicks Likes Sensor data Object Storage Databases Data warehouse Streaming analytics BI Hadoop Spark/Presto Elasticsearch Artificial Intelligence
- 8. A Flywheel For Data More Data Better Analytics Better ProductsMore Users Click stream User activity Generated content Purchases Clicks Likes Sensor data Object Storage Databases Data warehouse Streaming analytics BI Hadoop Spark/Presto Elasticsearch Artificial Intelligence
- 9. • Artificial Intelligence: design software applications which exhibit human-like behavior, e.g. speech, natural language processing, reasoning or intuition • Machine Learning: teach machines to learn without being explicitly programmed • Deep Learning: using neural networks, teach machines to learn from complex data where features cannot be explicitly expressed
- 10. Amazon.com,1995
- 11. Artificial Intelligence At Amazon Thousands Of Employees Across The Company Focused on AI Discovery & Search Fulfilment & Logistics Enhance Existing Products Define New Categories Of Products Bring Machine Learning To All
- 12. 25,000 skills
- 13. Machine Learning for customer support Not all customer interactions can be solved in a self-service mode. Therefore, Amazon operates large customer support centers where Customer Service Representatives (CSR) handle customer requests. The machine learning models described above are used to optimize the human interactions of these requests. For example, they are used to route the customer call to the best CSR before the customer has even started to speak! They are also used again during the call.
- 14. Selected customers running AI on AWS
- 16. As soon as 2018, Alexa will be your companion in BMWs https://techcrunch.com/2017/09/27/bmw-to-bring-alexa-to-its-cars-starting-in-2018/
- 17. https://www.oreilly.com/ideas/self-driving-trucks-enter-the-fast-lane-using-deep-learning Last June, tuSimple drove an autonomous truck for 200 miles from Yuma, AZ to San Diego,
- 18. Amazon AI Intelligent Services Powered By Deep Learning
- 19. The Advent Of Deep Learning Algorithms
- 20. The Advent Of Deep Learning Data Algorithms
- 21. The Advent Of Deep Learning Data GPUs & Acceleration Algorithms
- 22. The Advent Of Deep Learning Data GPUs & Acceleration Programming models Algorithms
- 23. Can We Help Customers Put Intelligence At The Heart Of Every Application & Business?
- 24. Questions, questions… What’s the business problem my IT has failed to solve Should I design and train my own Deep Learning model? Should I use a pre-trained model? Should I use a SaaS solution? Same questions as “Big Data” years ago
- 25. Amazon AI for every developer
- 26. Amazon AI: Three New Deep Learning Services Polly Life-like Speech
- 27. Amazon AI: Three New Deep Learning Services Rekognition Life-like Speech Image Analysis Polly
- 28. Amazon AI: Three New Deep Learning Services Rekognition Lex Life-like Speech Image Analysis Conversational Engine Polly
- 29. Amazon AI: Three New Deep Learning Services Polly Rekognition Lex Life-like Speech Image Analysis Conversational Engine
- 30. Polly: Life-like Speech Service Converts text to life-like speech 48 voices 24 languages Low latency, real time Fully managed
- 31. “Today in Seattle, WA, it’s 11°F” ‘"We live for the music" live from the Madison Square Garden.’ 1. Automatic, Accurate Text Processing Polly: A Focus On Voice Quality & Pronunciation
- 32. Polly: A Focus On Voice Quality & Pronunciation 2. Intelligible and Easy to Understand 1. Automatic, Accurate Text Processing
- 33. 2. Intelligible and Easy to Understand 3. Add Semantic Meaning to Text “Richard’s number is 2122341237“ “Richard’s number is 2122341237“ Telephone Number Polly: A Focus On Voice Quality & Pronunciation 1. Automatic, Accurate Text Processing
- 34. 2. Intelligible and Easy to Understand 3. Add Semantic Meaning to Text 4. Customized Pronunciation “My daughter’s name is Kaja.” “My daughter’s name is Kaja.” Polly: A Focus On Voice Quality & Pronunciation 1. Automatic, Accurate Text Processing
- 35. Polly: Life-like Speech Service High quality, through best-in-class deep learning Deep functionality Easy to use & thoughtfully integrated Built for production Low cost
- 36. Amazon AI: Three New Deep Learning Services Polly Rekognition Lex Life-like Speech Image Analysis Conversational Engine
- 37. Rekognition: Search & Understand Visual Content Real-time & batch image analysis Object & Scene Detection Facial Detection Face SearchFacial Analysis
- 38. Rekognition: Object & Scene Detection Bay Beach Coast Outdoors Sea Water Palm_tree Plant Tree Summer Landscape Nature Hotel 99.18% 99.18% 99.18% 99.18% 99.18% 99.18% 99.21% 99.21% 99.21% 58.3% 51.84% 51.84% 51.24% Category Confidence
- 40. Rekognition: Facial Analysis Emotion: calm: 73% Sunglasses: false (value: 0) Gender: female (value: 0) Mouth open wide: 0% (value: 0) Eye closed: open (value: 0) Glasses: no glass (value: 0) Mustache: false (value: 0) Beard: no (value: 0)
- 42. Rekognition: Facial Search Facial verification Face Search Visual Similarity Search (compare two faces) (compare many faces) (find similar faces)
- 43. High quality, through best-in-class deep learning Deep functionality Easy to use & thoughtfully integrated Built for production Low cost Rekognition: Search & Understand Visual Content
- 44. DEMO
- 45. Amazon AI: Three New Deep Learning Services Polly Rekognition Lex Life-like Speech Image Analysis Conversational Engine
- 46. Lex: Build Natural, Conversational Interactions In Voice & Text Voice & Text “Chatbots” Powers Alexa Voice interactions on mobile, web & devices Text interaction with Slack & Messenger Enterprise Connectors (with more coming) Salesforce Microsoft Dynamics Marketo Zendesk Quickbooks Hubspot
- 48. Origin Destination Departure Date Flight Booking “Book a flight to London”
- 49. Origin Destination Departure Date Flight Booking “Book a flight to London” Automatic Speech Recognition Book Flight London
- 50. Origin Destination Departure Date Flight Booking “Book a flight to London” Automatic Speech Recognition Natural Language Understanding Book Flight London Grammar Graph Utterances Knowledge Graph Flight booking London Heathrow
- 51. Origin Destination London Heathrow Departure Date Flight Booking “Book a flight to London” Automatic Speech Recognition Natural Language Understanding Book Flight London Grammar Graph Utterances Knowledge Graph Flight booking London Heathrow
- 52. Origin Seattle Destination London Heathrow Departure Date Flight Booking “Book a flight to London” Automatic Speech Recognition Natural Language Understanding Book Flight London Grammar Graph Utterances Knowledge Graph Flight booking London Heathrow LocationLocation
- 53. Origin Seattle Destination London Heathrow Departure Date Flight Booking “Book a flight to London” Automatic Speech Recognition Natural Language Understanding Book Flight London Grammar Graph Utterances Knowledge Graph Flight booking London Heathrow Prompt LocationLocation “When would you like to fly?”
- 54. Origin Seattle Destination London Heathrow Departure Date Flight Booking “Book a flight to London” Automatic Speech Recognition Natural Language Understanding Book Flight London Grammar Graph Utterances Knowledge Graph Flight booking London Heathrow Prompt LocationLocation “When would you like to fly?” “When would you like to fly?” Polly
- 55. Origin Seattle Destination London Heathrow Departure Date Flight Booking “Next Friday” “When would you like to fly?”
- 56. Origin Seattle Destination London Heathrow Departure Date Flight Booking “Next Friday” Automatic Speech Recognition Next Friday
- 57. Origin Seattle Destination London Heathrow Departure Date 11/18/2016 Flight Booking “Next Friday” Automatic Speech Recognition Natural Language Understanding Next Friday Grammar Graph Utterances Knowledge Graph Flight booking 11/18/2016
- 58. Origin Seattle Destination London Heathrow Departure Date 11/18/2016 Flight Booking “Next Friday” Automatic Speech Recognition Natural Language Understanding Next Friday Grammar Graph Utterances Knowledge Graph Flight booking 11/18/2016
- 59. Origin Seattle Destination London Heathrow Departure Date 11/18/2016 Flight Booking “Next Friday” Automatic Speech Recognition Natural Language Understanding Next Friday Grammar Graph Utterances Knowledge Graph Flight booking 11/18/2016 Confirmation “Your flight is booked for next Friday”
- 60. Origin Seattle Destination London Heathrow Departure Date 11/18/2016 Flight Booking “Next Friday” Automatic Speech Recognition Natural Language Understanding Next Friday Grammar Graph Utterances Knowledge Graph Flight booking 11/18/2016 “Your flight is booked for next Friday” Confirmation “Your flight is booked for next Friday” Polly
- 61. Origin Seattle Destination London Heathrow Departure Date 11/18/2016 Flight Booking “Next Friday” Automatic Speech Recognition Natural Language Understanding Next Friday Grammar Graph Utterances Knowledge Graph Flight booking 11/18/2016 Hotel Booking
- 62. High quality, through best-in-class deep learning Deep functionality Easy to use & thoughtfully integrated Built for production Low cost Lex: Build Natural, Conversational Interactions In Voice & Text
- 63. Amazon AI for every developer
- 64. Amazon Machine Learning • Easy-to-use, managed machine learning service built for developers • Robust, powerful machine learning technology based on Amazon’s internal systems • Create prediction and classification models using your data already stored in the AWS Cloud • Deploy models to production in seconds
- 65. Fully managed model and prediction services End-to-end service, with no servers to provision and manage One-click production model deployment Programmatically query model metadata to enable automatic retraining workflows Monitor prediction usage patterns with Amazon CloudWatch metrics
- 66. ” “ Fraud.net Uses AWS to Quickly, Easily Detect Online Fraud Fraud.net is the world’s leading crowdsourced fraud prevention platform. Amazon Machine Learning helps us reduce complexity and make sense of emerging fraud patterns. • Needed to build and train a larger number of more targeted machine-learning models • Uses Amazon Machine Learning to provide more than 20 models • Easily builds and trains models to effectively detect online payment fraud • Reduces complexity and makes sense of emerging fraud patterns • Saves clients $1 million weekly by helping them detect and prevent fraud Oliver Clark CTO, Fraud.net ” “
- 67. ” “ Upserve Uses AWS to Help Restaurants Predict Business Upserve provides online payment and analytical software to thousands of restaurant owners throughout the U.S. Using Amazon Machine Learning, we can predict the total number of customers who will walk through a restaurant’s doors in a night. • Needed its restaurant management platform to provide more predictive analytics • Builds and trains more than 100 machine learning models weekly • Streams restaurant sales and menu item data in real time • Helps restaurateurs predict nightly business Bright Fulton Director of Infrastructure Engineering, ” “
- 68. Amazon AI for every developer
- 69. Apache MXNet: Open Source library for Deep Learning Programmable Portable High Performance Near linear scaling across hundreds of GPUs Highly efficient models for mobile and IoT Simple syntax, multiple languages Most Open Best On AWS Optimized for Deep Learning on AWS Accepted into the Apache Incubator More information at mxnet.io
- 70. Input Output 1 1 1 1 0 1 0 0 0 3 mx. sym. Convol ut i on( dat a, ker nel =( 5, 5) , num_f i l t er =20) mx. sym. Pool i ng( dat a, pool _t ype=" max" , ker nel =( 2, 2) , st r i de=( 2, 2) l st m. l st m_unr ol l ( num_l st m_l ayer , seq_l en, l en, num_hi dden, num_embed) 4 2 2 0 4=Max 1 3 ... 4 0.2 -0.1 ... 0.7 mx. sym. Ful l yConnect ed( dat a, num_hi dden=128) 2 mx. symbol . Embeddi ng( dat a, i nput _di m, out put _di m = k) 0.2 -0.1 ... 0.7 Queen 4 2 2 0 2=Avg Input Weights cos(w, queen ) = cos(w, k i n g) - cos(w, m an ) + cos(w, w om an ) mx. sym. Act i vat i on( dat a, act _t ype=" xxxx" ) " r el u" " t anh" " si gmoi d" " sof t r el u" Neural Art Face Search Image Segmentation Image Caption “ People Riding Bikes” Bicycle, People, Road, Sport Image Labels Image Video Speech Text “ People Riding Bikes” Machine Translation “ Οι άνθρωποι ιππασίας ποδήλατα” Events mx. model . FeedFor war d model . f i t mx. sym. Sof t maxOut put
- 71. MXNet: Scalable Deep Learning Framework
- 72. One-Click GPU Deep Learning AWS Deep Learning AMI Up to~40k CUDA cores Apache MXNet TensorFlow Theano Caffe Torch Pre-configured CUDA drivers Anaconda, Python3 + CloudFormation template + Container Image
- 73. DEMO
- 74. Amazon AI for every developer
- 75. AWS is the Center of Gravity for Artificial Intelligence More information at http://aws.amazon.com/ai
- 76. Thank you! Thank you! Julien Simon AI Evangelist, EMEA @julsimon http://medium.com/@julsimon
Editor's Notes
- Using AWS, C-SPAN can sample a frame every six seconds for recognition against indexed faces in a database of 97,000 people. Previously, this was done manually: Indexers scrolled through screen captures to identify who was speaking at any given point and select an image to represent each individual in each video. C-SPAN expects to save 8,000 to 9,000 hours a year in labor by automating that process using Rekognition, and will be able to index 100% of its incoming footage and archives.
- Today, we have announced Amazon ML, the newest addition to the Amazon Web Services family. Amazon ML is easy to use, and intended for developers – people who are already most connected and familiar with data instrumentation, pipelines and storage/ Amazon ML is based on the same robust ML technology that is already used within Amazon’s internal systems, generating billions of predictions weekly Amazon ML is built to make it simple and reliable to use the data that you are already storing in the AWS cloud, in products like Amazon S3, Amazon Redshift and Amazon RD And lastly, Amazon ML is built to eliminate the gap between having models and using these models to build smart applications. Production deployment is only a click away – and sometimes you won’t even need that one click.
- I want to start by talking about ease of use, because it was so incredibly important to us as we built this product. We wanted to make machine learning accessible to all AWS developers. What we know from both our internal experience and speaking with our customers is that using machine learning to build real-world falls into two types. The first stage is experimentation: trying out different data sources, different ways to transform data, different ways to use ML technology to approach a problem. In this stage, it is really helpful to work with interactive tools that let you do many experiments easily. The second stage is productizing the models. This involves automating model training, deployment and use – for example, tying the prediction generation activities with your application’s business logic. And there is just no substitute for having a capable API to achieve this – that enables the ML piece to act just as robustly as the rest of your application. Most ML toolkits out there do one or the other. We knew that to enable AWS developers to easily use ML we needed to support both types of ML development. And so what you will see in Amazon ML is a powerful service console that enables you to connect to your data source, to train new models, to measure their quality, and to manage these models easily, without writing any code. This enables you to spend more time thinking about your business problem and your data, not tinkering with the technology pieces. Once you have models that are ready to be productized, you have a full API available to deploy these models, to query them for predictions, and to train and evaluate new models on schedule. It’s a complete lifecycle. We have included Amazon ML API support into all the major AWS SDKs. And, we have also bundled prediction querying capability into the AWS mobile SDKs, making it really simple to create mobile applications that use predictions.
- Next, let’s talk about technology. Amazon ML is based on the same ML technology that has long been deployed within Amazon, and is used to generate tens of billions of predictions weekly. When I say technology, I mean not just the learning algorithm – which is important but by no means only part of ML systems. Amazon Machine Learning comes with technology that suggests data transformations based on your data that will improve model’s quality – and you are able to use these transformations as they are, or adjust the transformation instructions without writing any data transforming code. Amazon ML also includes functionality to provide alerts when known pitfalls are encountered in your input data – for example, when some attributes have many missing values, or when the model was evaluated with data that is significantly different from what was used to train it. This – ensuring that data is evaluated on a fair dataset is an example of an industry best practice that we have built into the product, among many others, all around the goal of making the resulting models more powerful. Finally, a word about speed and scale. Amazon ML can create models from up to 100 GB of data. You can use it to generate billions of predictions, and obtain them in batches or real-time. I will get to the prediction interfaces soon.
- STORY BACKGROUND Fraud.net uses Amazon Machine Learning to support its machine-learning models. The company uses Amazon DynamoDB and AWS Lambda to run code without provisioning and managing servers. Uses Amazon Redshift for data analysis. SOLUTION & BENEFITS Launches and trains machine-learning models in almost half the time it took on other platforms. Reduces complexity and makes sense of emerging fraud patterns. Saves customers $1 million each week. CONTENT TAGS Main use case: Big Data, Analytics, & Business Intelligence (BI) Keywords: online fraud, fraud detection, machine learning, big data, Amazon Machine Learning, business intelligence AWS Services used: Amazon DynamoDB, Amazon Redshift, Amazon Machine Learning, Amazon S3, AWS Lambda Benefits Realized: Agility, Better Performance, Ease of Use, Lower Cost, Reliability, Scalability/Elasticity, Speed
- STORY BACKGROUND Provider of cloud-based restaurant management platform Wanted to help restaurant owners gain predictive analytics Sought to take advantage of machine learning technology SOLUTION & BENEFITS Uses Amazon Machine Learning to provide predictive analysis through its Shift Prep application Builds and trains more than 100 machine learning models each week Helps restaurant owners predict nightly business CONTENT TAGS Main use case: Big Data, Analytics, & Business Intelligence (BI) Keywords: restaurants, predictive analysis, machine learning, Amazon Machine Learning, online payment process AWS Services used: Amazon EC2 Container Service, Amazon Data Pipeline, Amazon Machine Learning, Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon Elastic Map Reduce Benefits Realized: Ease of Use, Lower Time-to-Market, Reliability, Scalability/Elasticity, Speed, User Experience