AWS re:Invent 2016

The PanCancer Analysis of Whole Genomes (PCAWG) project is a large-scale, highly distributed research collaboration designed to identify common patterns of mutations across 2,800 cancer genomes. The use of public and private clouds were instrumental in analyzing this dataset using current best practice containerized pipelines. This session describes the technical infrastructure built for the project, how we leveraged cloud environments to perform the “core” analysis, and the lessons learned along the way.

Jump-start your machine learning project by using the crowd to build your training set. Before you can train your machine learning algorithm, you need to take your raw inputs and label, annotate, or tag them to build your ground truth. Learn how to use the Amazon Mechanical Turk marketplace to perform these tasks. We share Amazon's best practices, developed while training our own machine learning algorithms, and walk you through quickly getting affordable and high-quality training data.

Neural networks have a long and rich history in automatic speech recognition. In this talk, we present a brief primer on the origin of deep learning in spoken language, and then explore today’s world of Alexa. Alexa is the AWS service that understands spoken language and powers Amazon Echo. Alexa relies heavily on machine learning and deep neural networks for speech recognition, text-to-speech, language understanding, and more. We also discuss the Alexa Skills Kit, which lets any developer teach Alexa new skills.

This session will introduce you to Amazon Rekognition, a new service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API lets you easily build powerful visual search and discovery into your applications. With Amazon Rekognition, you only pay for the images you analyze and the face metadata you store. There are no minimum fees and there are no upfront commitments.

This session will introduce you to Amazon Polly, a new deep learning service that turns text into lifelike speech. Polly enables existing applications to speak as a first class feature and creates the opportunity for entirely new categories of speech-enabled products – from mobile apps and cars, to devices and appliances. Polly includes 47 lifelike voices and support for 24 languages, so you can select the ideal voice and distribute your speech-enabled applications in many geographies. Polly is easy to use – you just send the text you want converted into speech to the Polly API, and Polly immediately returns the audio stream to your application so you can play it directly or store it in a standard audio file format, such as MP3. Polly supports Speech Synthesis Markup Language (SSML) tags like prosody so you can adjust the speech rate, pitch, or volume. Polly is a secure service that delivers all of these benefits at high scale and at low latency. You can cache and replay Polly’s generated speech at no additional cost. Polly lets you convert 5M characters per month for free during the first year. Polly’s pay-as-you-go pricing, low cost per request, and lack of restrictions on storage and reuse of voice output make it a cost-effective way to enable speech synthesis everywhere. Join this session to learn more and find out how you get can started with Amazon Polly, today!

Deep learning continues to push the state of the art in domains such as video analytics, computer vision, and speech recognition. Deep networks are powered by amazing levels of representational power, feature learning, and abstraction. This approach comes at the cost of a significant increase in required compute power, which makes the AWS cloud an excellent environment for training. Innovators in this space are applying deep learning to a variety of applications. One such innovator, Vilynx, a startup based in Palo Alto, realized that the current pre-roll advertising-based models for mobile video weren’t returning publishers' desired levels of engagement. In this session, we explain the algorithmic challenges of scaling across multiple nodes, and what Intel is doing on AWS to overcome them. We describe the benefits of using AWS CloudFormation to set up a distributed training environment for deep networks. We also showcase Vilynx’s contributions to video discoverability, and explain how Vilynx uses AWS tools to understand video content. This session is sponsored by Intel.

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.

The Howard Hughes Corporation partnered with 47Lining to develop a managed enterprise data lake based on Amazon S3. The purpose of the managed EDL is to fuse relevant on-premises and third-party data to enable Howard Hughes to answer its most valuable business questions. Their first analysis was a lead-scoring model that uses Amazon Machine Learning (Amazon ML) to predict propensity to purchase high-end real estate. The model is based on a combined set of public and private data sources, including all publicly recorded real estate transactions in the US for the past 35 years. By changing their business process for identifying and qualifying leads to use the results of data-driven analytics from their managed data lake in AWS, Howard Hughes increased the number of identified qualified leads in their pipeline by over 400% and reduced the acquisition cost per lead by more than 10 times. In this session, you will see a practical example of how to use Amazon ML to improve business results, how to architect a data lake with Amazon S3 that fuses on-premises, third-party, and public data sets, and how to train and run an Amazon ML model to attain predictive accuracy.

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.

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.