AWS re:Invent 2016

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AWS re:Invent 2016 Keynote: Andy Jassy Dec 25, 2016

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CEO Andy Jassy discusses the latest AWS news and announcements.

MAC206: Machine Learning State of the Union Mini Con Dec 25, 2016

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With the growing number of business cases for artificial intelligence (AI), machine learning (ML) and deep learning (DL) continue to drive the development of cutting edge technology solutions. We see this manifested in computer vision, predictive modeling, natural language understanding, and recommendation engines. During this full afternoon of sessions and workshops, learn how you can develop your own applications to leverage the benefits of these services. Join this State of the Union presentation to hear more about ML and DL at AWS and see how Motorola Solutions is leveraging these state-of-the-art technologies to solve public safety challenges, and how Ohio Health intends to inject AI into the medical system.

MAE303: Discovery Channel's Broadcast Workflows and Channel Origination on AWS Dec 25, 2016

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Media delivery requirements are continually changing, driven by accelerating mobile, tablet, smart TV, and set-top technology advances. Broadcasters need agile solutions to the changing media and entertainment landscape that don't require multiyear projects with large upfront investments. In this session, we walk through Discovery Communications' migration of its broadcast playout and channel origination to AWS. Discovery Communications is a leader in nonfiction media, reaching more than 3 billion cumulative viewers in 220 countries and territories. Traditionally, broadcast origination for content delivered to telecommunications companies, cable TV, and satellite has existed only in on-premises data centers. In this session, we walk through Discovery's migration of broadcast playout supporting hundreds of channels worldwide to AWS. We show how Discovery has not only reduced their TCO but also has improved their agility by launching new channels on demand. We also walk through how channel origination is being deployed in a secure, automated fashion, and with a level of high availability that exceeds what is possible in a traditional data center.

MAE302: Turner's cloud native media supply chain for TNT, TBS, Adult Swim, Cartoon Network, CNN Dec 25, 2016

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As Turner continues to make the transition from a traditional broadcast organization to a consumer-centric, data-driven media company, we are being challenged to re-think our approach to content supply. There is a need to achieve new levels of agility, flexibility and scalability to meet the rapidly evolving demands of our top media brands - including TBS, TNT, Cartoon Network, Adult Swim and CNN. To that end, we are transitioning the infrastructure that acquires, processes and distributes media for consumer-facing systems to the cloud. At the core of this environment is our Supply Chain Management application. The SCM app provides business and technical process management via an HTML based UI framework, State Machine, Rules Engine, Cost Model, Forms Service. We took advantage of several AWS specific services, including Lambda, S3, Dynamo DB, SNS, Elastic, Cloud Formation and Code Commit. The entire system is instance-less with all application code running in either the browser or within Lambda's. To ease development and debugging we created a method to run all JS libraries in the browser, switching to Lambda when we deploy with Code Commit. Cloud media processing infrastructure is BEING created on demand via an integration with SDVI. The SDVI and SCM apps exchange events and data via SNS and S3.

MAE301: Accelerating the Transition to Broadcast and OTT Infrastructure in the Cloud: Spotlight on Building Media Services on AWS and Elemental Dec 25, 2016

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In this session, we show how to seamlessly transition VOD, live, and other advanced media workflows from on-premises deployments to the cloud. Cinépolis will provide an overview of their transcoding solution on AWS and how they have seamlessly expanded the solution increasing their customer reach. We'll show real world examples of the API calls used to configure and control all elements of the workflow including compression and origination. And how standard AWS services can be media-optimized with Elemental Technologies to form a robust live solution.

MAC403: Automatic Grading of Diabetic Retinopathy through Deep Learning Dec 25, 2016

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Diabetic retinopathy (DR) is the leading cause of blindness for people aged 20 to 64, and afflicts more than 120 million people worldwide. Fortunately, vigilant monitoring greatly improves the chance to preserve one’s eyesight. This work used deep learning to analyze images of the retina and fundus for automated diagnosis of DR on a grading scale from 0 (normal) to 4 (severe). We achieved substantial improvement in accuracy compared to traditional approaches and continued advances by using a small auxiliary dataset that provided low-effort, high-value supervision. Data for training and testing, provided by the 2015 Kaggle Data Science Competition with over 80,000 high resolution images (>4 megapixels), required Amazon EC2 scalability to provide the GPU hardware needed to train a convolutional network with over 2 million parameters. For the competition, we focused on accurately modeling the scoring system, penalizing bad mistakes more severely, and combatting the over-prevalence of grade-0 examples in the dataset. We explored ideas first at low resolution on low-cost single-GPU instances. After finding the best methodology, we showed it could be scaled to equivalent improvements at high resolution, using the more expensive quad-GPU instances more effectively. This prototype model placed 15 out of 650 teams across the world with a kappa score of 0.78. We’ve now advanced the model via a new architecture that integrates the prototype and a new network specialized in finding dot hemorrhages, critical to identifying early DR. By annotating a small set of 200 images for hemorrhages, the performance jumped to a kappa of 0.82. We believe strategies that employ a bit more supervision for more effective learning are pivotal for cracking deep learning’s greatest weakness: its voracious appetite for data.

MAC307: Predicting Customer Churn with Amazon Machine Learning Dec 25, 2016

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In this session, we take a specific business problem—predicting Telco customer churn—and explore the practical aspects of building and evaluating an Amazon Machine Learning model. We explore considerations ranging from assigning a dollar value to applying the model using the relative cost of false positive and false negative errors. We discuss all aspects of putting Amazon ML to practical use, including how to build multiple models to choose from, put models into production, and update them. We also discuss using Amazon Redshift and Amazon S3 with Amazon ML.

MAC306: Using MXNet for Recommendation Modeling at Scale Dec 25, 2016

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For many companies, recommendation systems solve important machine learning problems. But as recommendation systems grow to millions of users and millions of items, they pose significant challenges when deployed at scale. The user-item matrix can have trillions of entries (or more), most of which are zero. To make common ML techniques practical, sparse data requires special techniques. Learn how to use MXNet to build neural network models for recommendation systems that can scale efficiently to large sparse datasets.

MAC304: NEW LAUNCH! Introducing Amazon Lex Dec 25, 2016

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Amazon Lex is a service for building conversational interfaces into any applications using voice and text. With Lex, the same deep learning engine that powers Amazon Alexa is now available to any developer, enabling you to build sophisticated, natural language chatbots into your new and existing applications. Amazon Lex provides the deep functionality and flexibility of natural language understanding (NLU) and automatic speech recognition (ASR) to allow you to build highly engaging user experiences with lifelike, conversational interactions. In this introductory session, find out how Lex provides deep functionality and flexibility to empower you to define entirely new categories of products that are made possible through conversational interfaces.

MAC303: Zillow Group: Developing Classification and Recommendation Engines with Amazon EMR and Apache Spark Dec 25, 2016

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Customers are adopting Apache Spark ‒ an open-source distributed processing framework ‒ on Amazon EMR for large-scale machine learning workloads, especially for applications that power customer segmentation and content recommendation. By leveraging Spark ML, a set of machine learning algorithms included with Spark, customers can quickly build and execute massively parallel machine learning jobs. Additionally, Spark applications can train models in streaming or batch contexts, and can access data from Amazon S3, Amazon Kinesis, Amazon Redshift, and other services. This session explains how to quickly and easily create scalable Spark clusters with Amazon EMR, build and share models using Apache Zeppelin and Jupyter notebooks, and use the Spark ML pipelines API to manage your training workflow. In addition, Jasjeet Thind, Senior Director of Data Science and Engineering at Zillow Group, will discuss his organization's development of personalization algorithms and platforms at scale using Spark on Amazon EMR.

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