AI and Innovations on AWS
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Slides from my talk on Machine Learning, Artificial Intelligence and Innovations on AWS.
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AI and Innovations on AWS
- 1. Adrian Hornsby | Technical Evangelist | AWS AI & Innovations on AWS
- 3. A system or service which can perform tasks that usually require human intelligence Artificial Intelligence
- 5. The Challenge For Artificial Intelligence: SCALE Data
- 6. Data Training The Challenge For Artificial Intelligence: SCALE
- 7. Data Training Prediction The Challenge For Artificial Intelligence: SCALE
- 8. Aggressive migration New data created on AWS Data Training Prediction PBs of existing data The Challenge For Artificial Intelligence: SCALE
- 9. Tons of GPUs Elastic capacity Training Prediction Pre-built images Aggressive migration New data created on AWS Data PBs of existing data The Challenge For Artificial Intelligence: SCALE
- 10. Tons of GPUs and CPUs Serverless At the Edge, On IoT Devices Prediction Tons of GPUs Elastic capacity Training Pre-built images Aggressive migration New data created on AWS Data PBs of existing data The Challenge For Artificial Intelligence: SCALE
- 11. New Features for Existing Products The Promise Of Artificial Intelligence: INNOVATION
- 12. New Experiences and Product Categories New Features for Existing Products The Promise Of Artificial Intelligence: INNOVATION
- 13. Breakthrough Advances New Experiences and Product Categories New Features for Existing Products The Promise Of Artificial Intelligence: INNOVATION
- 21. Text In, Life-like Speech Out Amazon Polly “Today in Seattle, WA it’s 11°F” “Today in Seattle Washington it’s 11 degrees Fahrenheit”
- 22. “Today in Seattle, WA, it’s 11°F” ‘"We live for the music" live from the Madison Square Garden.’ 1. Automatic, Accurate Text Processing A Focus On Voice Quality & Pronunciation
- 23. 2. Intelligible and Easy to Understand 1. Automatic, Accurate Text Processing A Focus On Voice Quality & Pronunciation
- 24. 2. Intelligible and Easy to Understand 3. Add Semantic Meaning to Text “Richard’s number is 2122341237“ “Richard’s number is 2122341237“ Telephone Number 1. Automatic, Accurate Text Processing A Focus On Voice Quality & Pronunciation
- 25. 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.” 1. Automatic, Accurate Text Processing A Focus On Voice Quality & Pronunciation
- 26. Duolingo voices its language learning service Using Polly Duolingo is a free language learning service where users help translate the web and rate translations. With Amazon Polly our users benefit from the most lifelike Text-to-Speech voices available on the market. Severin Hacker CTO, Duolingo ” “ • Spoken language crucial for language learning • Accurate pronunciation matters • Faster iteration thanks to TTS • As good as natural human speech
- 27. Rekognition Image Analysis Artificial Intelligence Services on AWS
- 28. Object & Scene Detection
- 29. Object & Scene Detection
- 30. Facial Analysis
- 31. Facial Analysis
- 32. Facial Search
- 33. Facial Search
- 34. Rekognition Customers Media and Entertainment Public Safety Law Enforcement Digital Asset Management Influencer Marketing Digital Advertising Education Consumer Storage
- 35. Media Case Study Identify who is on camera at what time for each of 8 networks so that recorded video streams can be indexed and searched Video frame-sampling facial recognition solution using Amazon Rekognition: • Indexed 97,000 people into a face collection in 1 day • Sample frames every 6 secs and test for image variance • Upload images to S3 and call Rekognition to find best facial match • Store time stamp and faceID metadata
- 36. Influencer Marketing Case Study Associate influencers with objects and scenes in social media images in order to create high impact campaigns for clients Using Rekognition for metadata extraction: • Create rich media indexes of images from social media feeds, which the application associates with influencers • Enable analytics to profile environments where influence is strongest • Connect client brands with the influencers most likely to have impact
- 37. Bynder allows you to easily create, find and use content for branding automation and marketing solutions. With our new AI capabilities, Bynder’s software… now allows users to save hours of admin labor when uploading and organizing their files, adding exponentially more value. Chris Hall CEO, Bynder ” “ With Rekognition, Bynder revolutionizes marketing admin tasks with AI capabilities
- 40. Lex ASR & NLU Artificial Intelligence Services on AWS
- 41. Amazon Lex Utterances Spoken or typed phrases that invoke your intent BookHotel Intents An Intent performs an action in response to natural language user input Slots Slots are input data required to fulfill the intent Fulfillment Fulfillment mechanism for your intent
- 42. “Book a Hotel” Book Hotel NYC “Book a Hotel in NYC” Automatic Speech Recognition Hotel Booking New York City Natural Language Understanding Intent/Slot Model UtterancesHotel Booking City New York City Check In Nov 30th Check Out Dec 2nd “Your hotel is booked for Nov 30th” Polly Confirmation: “Your hotel is booked for Nov 30th” “Can I go ahead with the booking? a in
- 45. Amazon Machine Learning Predictive Analytics Artificial Intelligence Platforms on AWS
- 46. Amazon ML Supervised Learning Algorithms Binary classification (Logistic regression) Multi-category classification (Multinomial logistic regression) Regression (Linear regression)
- 49. And a few more examples… Fraud detection Detecting fraudulent transactions, filtering spam emails, flagging suspicious reviews, … Personalization Recommending content, predictive content loading, improving user experience, … Targeted marketing Matching customers and offers, choosing marketing campaigns, cross-selling and up-selling, … Content classification Categorizing documents, matching hiring managers and resumes, … Churn prediction Finding customers who are likely to stop using the service, free-tier upgrade targeting, … Customer support Predictive routing of customer emails, social media listening, …
- 52. Amazon EMR Elastic MapReduce Artificial Intelligence Platforms on AWS
- 53. ML Applications on Amazon EMR
- 57. Deep Learning AMI Popular Frameworks Artificial Intelligence Frameworks on AWS
- 58. One-Click Deep Learning AWS Deep Learning AMIs Amazon Linux & Ubuntu Up to~40k CUDA cores Apache MXNet TensorFlow Theano Keras Caffe CNTK Torch Pre-configured CUDA drivers Anaconda, Python3 Out-of-the-box Tutorials + CloudFormation template + Container Image Available in the AWS Marketplace
- 59. 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 Apache MXNet
- 60. Integrate with AWS Services Bring Scalable Deep Learning to AWS Services such as Amazon EMR, AWS Lambda and Amazon ECS. Foundation for AI Services AmazonAI API Services, Internal AI Research, Amazon Core AI Development Leverage the Community Community brings velocity and innovation with no single project owner or controller Amazon Strategy | Apache MXNet
- 61. 0.2 -0.1 ... 0.7 Input Output 1 1 1 1 0 1 0 0 0 3 mx.sym.Pooling(data, pool_type="max", kernel=(2,2), stride=(2,2) lstm.lstm_unroll(num_lstm_layer, seq_len, len, num_hidden, num_embed) 4 2 2 0 4=Max 1 3 ... 4 0.2 -0.1 ... 0.7 mx.sym.FullyConnected(data, num_hidden=128) 2 mx.symbol.Embedding(data, input_dim, output_dim = k) Queen 4 2 2 0 2=Avg Input Weights cos(w, queen) = cos(w, king) - cos(w, man) + cos(w, woman) mx.sym.Activation(data, act_type="xxxx") "relu" "tanh" "sigmoid" "softrelu" 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.FeedForward model.fit mx.sym.SoftmaxOutput Anatomy of a Deep Learning Model mx.sym.Convolution(data, kernel=(5,5), num_filter=20) Deep Learning Models
- 63. Real Time, Per Pixel Object Segmentation
- 66. Create your own Basquiat with Deep Learning https://becominghuman.ai/create-your-own-basquiat-with-deep-learning-for-much-less-than-110-million-314aa07c9ba8
- 67. Early Detection of Diabetic Complications
- 68. Skin Cancer Detection At Physician-Levels (or better)
- 69. Lung Cancer Detection With Deep Learning & Medical Imaging
- 70. Lung Cancer Detection With Deep Learning & Medical Imaging
- 72. Elastic GPUs On EC2 P2M4 D2 X1 G2T2 R4 I3 C5 General Purpose GPUGeneral Purpose Dense storage Large memory Graphics intensive Memory intensive High I/O Compute intensiveBurstable Lightsail Simple VPS F1 FPGAs Instance Families
- 73. 1GiB GPU Memory 2 GiB 4 GiB 8 GiB C u r r e n t G e n e r a t i o n E C 2 I n s t a n c e Elastic GPUs For EC2: GPU Acceleration For Graphics Workloads
- 74. FPGA Images Available In AWS Marketplace F 1 I n s t a nc e W i t h y o u r c u s t o m l o g i c r u n n i n g o n a n F P G A D e v e l o p , s i m u l a t e , d e b u g & c o m p i l e y o u r c o d e P a c k a g e a s F P G A I m a g e s F1 Instances: Bringing Hardware Acceleration To All
- 75. “Our results indicate that FPGAs may become the platform of choice for accelerating next-generation DNNs.”
- 76. Up to 40 thousand parallel processing cores 70 teraflops (single precision) over 23 teraflops (double precision) Instance Size GPUs GPU Peer to Peer vCPUs Memory (GiB) Network Bandwidth* p2.xlarge 1 - 4 61 1.25Gbps p2.8xlarge 8 Y 32 488 10Gbps p2.16xlarge 16 Y 64 732 20Gbps *In a placement group Amazon EC2 P2 Instances
- 80. Thank You!
Editor's Notes
- 45
- Also feel free to also contact me on twitter or my email and I will do my best to connect you with the right folks.
- as a system or service which can perform tasks that usually require human intelligence, such as visual perception, speech recognition, decision-making or translation
- as a system or service which can perform tasks that usually require human intelligence, such as visual perception, speech recognition, decision-making or translation
- Training requires a ton of compute power Specifically GPUs, which are super expensive And which can sit idle when you’re not training them
- Scale of prediction Netflix and cost of implementation Prediction for all models Prediction at the edge/on device
- Today Ai is everywhere is Amazon From recommendation pages, to fulfillment centers, Prime Air Drones, Amazon Go and Alexa
- Today Ai is everywhere is Amazon From recommendation pages, to fulfillment centers, Prime Air Drones, Amazon Go and Alexa
- Amazon Web Services provides a rich ecosystem to help you build smarter applications. In this context, it is worth highlighting the higher level AI services based on deep learning algorithms, like Amazon Rekognition, an image recognition service, Amazon Polly, a text to speech synthesizer, and Amazon Lex, a voice and text chatbot service. We also provide the infrastructure including GPU EC2 instances for fast parallel processing which you can use in combination with any of the popular deep learning libraries like Apache mxnet, Tensorflow, Theano, etc, all of which are available on the AWS deep learning AMI. For your general machine learning purposes, you can also use EC2, Amazon Elastic MapReduce and Spark with SparkML to run any machine learning algorithm. Another popular library is the python scikit-learn, which you can deploy on AWS Lambda or containers, or EC2. So what I am trying to convey is that there is a lot of choice, which basically boils down to picking the right tool for the right job, where you can make trade-offs between ‘do your own’ with all the flexibility, or picking a managed solution which allows you to get results fast without having to do the heavy lifting.
- Amazon Web Services provides a rich ecosystem to help you build smarter applications. In this context, it is worth highlighting the higher level AI services based on deep learning algorithms, like Amazon Rekognition, an image recognition service, Amazon Polly, a text to speech synthesizer, and Amazon Lex, a voice and text chatbot service. We also provide the infrastructure including GPU EC2 instances for fast parallel processing which you can use in combination with any of the popular deep learning libraries like Apache mxnet, Tensorflow, Theano, etc, all of which are available on the AWS deep learning AMI. For your general machine learning purposes, you can also use EC2, Amazon Elastic MapReduce and Spark with SparkML to run any machine learning algorithm. Another popular library is the python scikit-learn, which you can deploy on AWS Lambda or containers, or EC2. So what I am trying to convey is that there is a lot of choice, which basically boils down to picking the right tool for the right job, where you can make trade-offs between ‘do your own’ with all the flexibility, or picking a managed solution which allows you to get results fast without having to do the heavy lifting.
- Today AWS AI offering is broadening – lets have a look at what we have got
- The basics are pretty simple, but the service has deep functionality. You can send the service a simple string of text, and it will generate the life like voice in your choice of 47 different voices. But it’s not naive of the context of the text. For example, the text here - ‘WA’ and ‘degree F’, that would sound strange if it were spoken out loud. Instead, Polly will automatically expand the text strings ‘WA’ and ‘degree F’, to ‘Washington’ and ‘degrees fahrenheit’, to create more life like speech. The developer doesn’t have to do anything - just send the text, and get life like voice back.
- Today AWS AI offering is broadening – lets have a look at what we have got
- Image moderation Rekognition automatically detects explicit or suggestive adult content in your images, and provides confidence scores.
- Clients can request influencers in a key demographic. Rekognition partners use their metadata to identify high quality influencers for targeted campaigns, which may involve paying influencers for product use and social media posts featuring the product. An influencer’s strength is measured by who is following them on social media Future use for Reko: identify brands, measure impact, rate influencers
- Today AWS AI offering is broadening – lets have a look at what we have got
- Amazon Web Services provides a rich ecosystem to help you build smarter applications. In this context, it is worth highlighting the higher level AI services based on deep learning algorithms, like Amazon Rekognition, an image recognition service, Amazon Polly, a text to speech synthesizer, and Amazon Lex, a voice and text chatbot service. We also provide the infrastructure including GPU EC2 instances for fast parallel processing which you can use in combination with any of the popular deep learning libraries like Apache mxnet, Tensorflow, Theano, etc, all of which are available on the AWS deep learning AMI. For your general machine learning purposes, you can also use EC2, Amazon Elastic MapReduce and Spark with SparkML to run any machine learning algorithm. Another popular library is the python scikit-learn, which you can deploy on AWS Lambda or containers, or EC2. So what I am trying to convey is that there is a lot of choice, which basically boils down to picking the right tool for the right job, where you can make trade-offs between ‘do your own’ with all the flexibility, or picking a managed solution which allows you to get results fast without having to do the heavy lifting.
- Binary Classification Model "Is this email spam or not spam?" "Will the customer buy this product?" "Is this product a book or a farm animal?" "Is this review written by a customer or a robot?" Multiclass Classification Model "Is this product a book, movie, or clothing?" "Is this movie a romantic comedy, documentary, or thriller?" "Which category of products is most interesting to this customer?" Regression Model "What will the temperature be in Seattle tomorrow?" "For this product, how many units will sell?" "What price will this house sell for?"
- Bookstore Home page 1996 (20 years) when we celebrated 1million titles
- Bookstore Home page 1996 (20 years) when we celebrated 1million titles
- There are lots of other examples. Machine learning is being used to filter spam emails, flag inappropriate content, personalize user experience, targeted marketing campaigns, call routing in support centers, social network monitoring, and many more.
- The platform protects more than 2 percent of all U.S. e-commerce, and its client base and data requirements are growing at a pace of more than 1,000 percent per year.
- to predict propensity to purchase high-end real estate. Some pretty impressive numbers – 400% increase in the number of identified qualified leads in their pipeline and more than 10x reduction is lead acquisition cost.
- to predict propensity to purchase high-end real estate. Some pretty impressive numbers – 400% increase in the number of identified qualified leads in their pipeline and more than 10x reduction is lead acquisition cost.
- Apache Spark and Spark ML overview Running Spark ML on Amazon EMR Interactive notebook options Building recommendation engines at Zillow Group
- Apache Spark and Spark ML overview Running Spark ML on Amazon EMR Interactive notebook options Building recommendation engines at Zillow Group
- Amazon Web Services provides a rich ecosystem to help you build smarter applications. In this context, it is worth highlighting the higher level AI services based on deep learning algorithms, like Amazon Rekognition, an image recognition service, Amazon Polly, a text to speech synthesizer, and Amazon Lex, a voice and text chatbot service. We also provide the infrastructure including GPU EC2 instances for fast parallel processing which you can use in combination with any of the popular deep learning libraries like Apache mxnet, Tensorflow, Theano, etc, all of which are available on the AWS deep learning AMI. For your general machine learning purposes, you can also use EC2, Amazon Elastic MapReduce and Spark with SparkML to run any machine learning algorithm. Another popular library is the python scikit-learn, which you can deploy on AWS Lambda or containers, or EC2. So what I am trying to convey is that there is a lot of choice, which basically boils down to picking the right tool for the right job, where you can make trade-offs between ‘do your own’ with all the flexibility, or picking a managed solution which allows you to get results fast without having to do the heavy lifting.
- 35 Lets have a look at the first offering – which gives you directly access to the lowest level of customization, our Deep Learning Image which let you use the most popular AI frameworks at a single click of the mouse.
- On click – Marketplace AMI supported and maintained by Amazon Web Services for use on EC2. Designed to provide a stable, secure, and high performance execution environment for deep learning applications running on Amazon EC2. Popular deep learning frameworks, including MXNet, Caffe, Tensorflow, Theano, CNTK and Torch as Packages that enable easy integration with AWS, including launch configuration tools and many popular AWS libraries and tools. It also includes the Anaconda Data Science Platform for Python2 and Python3. Just after this session, Julien will have a deep dive on MXNET, which is the Deep Learning platform of choice here at Amazon.
- $110 million
- Early detection
- 97% early to 14% late Every year there are about 5.4 million new cases of skin cancer in the United States, and while the five-year survival rate for melanoma detected in its earliest states is around 97 percent, that drops to approximately 14 percent if it’s detected in its latest stages
- Amazon Web Services provides a rich ecosystem to help you build smarter applications. In this context, it is worth highlighting the higher level AI services based on deep learning algorithms, like Amazon Rekognition, an image recognition service, Amazon Polly, a text to speech synthesizer, and Amazon Lex, a voice and text chatbot service. We also provide the infrastructure including GPU EC2 instances for fast parallel processing which you can use in combination with any of the popular deep learning libraries like Apache mxnet, Tensorflow, Theano, etc, all of which are available on the AWS deep learning AMI. For your general machine learning purposes, you can also use EC2, Amazon Elastic MapReduce and Spark with SparkML to run any machine learning algorithm. Another popular library is the python scikit-learn, which you can deploy on AWS Lambda or containers, or EC2. So what I am trying to convey is that there is a lot of choice, which basically boils down to picking the right tool for the right job, where you can make trade-offs between ‘do your own’ with all the flexibility, or picking a managed solution which allows you to get results fast without having to do the heavy lifting.
- It’s clear we love us some compute Workloads are not vanilla - they are of different sizes and constraints Like building a house: you don’t use just one tool, you use lots of tools in your toolbox. It’s also true with any of these building block services; the right tool for the job you need to get done.
- You can also add graphics processing which looks and operates just like a GPU. You can use the same OpenGL code that your application or game already uses, and have them rendered on a GPU. This is perfect if you only need a small part of a GPU more cost effectively (with the smallest option starting at just 1/8th of a GPU), or would like to be able to add graphics processing capabilities to instances which are optimized for I/O, storage, or memory workloads (scaling all the way up to connecting one or more full GPUs).
- 1/Develop, simulate, debug & compile your code 2/HW development kit and FPGA image 3/Create your own FPGA acceleration that you package into FPGA image 4/ Upload FPGA image 5/ MP
- You can also add graphics processing which looks and operates just like a GPU. You can use the same OpenGL code that your application or game already uses, and have them rendered on a GPU. This is perfect if you only need a small part of a GPU more cost effectively (with the smallest option starting at just 1/8th of a GPU), or would like to be able to add graphics processing capabilities to instances which are optimized for I/O, storage, or memory workloads (scaling all the way up to connecting one or more full GPUs).
- Summary