AWS re:Invent 2016

Latest Episodes:

LFS302: Continuous Compliance in the AWS Cloud for Regulated Life Sciences Applications within Merck Dec 25, 2016

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Life sciences organizations running regulated workloads in the cloud can move from point-in-space testing of their environment to near real-time testing to achieve continuous compliance with the mandates of auditors and regulation entities. Get deep insights into some of the AWS services used to accomplish continuous compliance such as Amazon CloudTrail, Amazon CloudWatch, AWS Config, Amazon VPC, Amazon S3, and Amazon EC2. Get real-world use cases of how heavily regulated environments within Merck maintain governance and control over a shared environment. We also discuss the automated tools used by Merck to eliminate manual processes and streamline IT management.

LFS303: How to Build a Big Data Analytics Data Lake Dec 25, 2016

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For discovery-phase research, life sciences companies have to support infrastructure that processes millions to billions of transactions. The advent of a data lake to accomplish such a task is showing itself to be a stable and productive data platform pattern to meet the goal. We discuss how to build a data lake on AWS, using services and techniques such as AWS CloudFormation, Amazon EC2, Amazon S3, IAM, and AWS Lambda. We also review a reference architecture from Amgen that uses a data lake to aid in their Life Science Research.

LFS304: Large-Scale, Cloud-Based Analysis of Cancer Genomes: Lessons Learned from the PCAWG Project Dec 25, 2016

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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.

MAC201: Getting to Ground Truth with Amazon Mechanical Turk Dec 25, 2016

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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.

MAC202: Deep Learning in Alexa Dec 25, 2016

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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.

MAC203: NEW LAUNCH! Introducing Amazon Rekognition Dec 25, 2016

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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.

MAC204: NEW LAUNCH! Introducing Amazon Polly Dec 25, 2016

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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!

MAC205: Deep Learning at Cloud Scale: Improving Video Discoverability by Scaling Up Caffe on AWS Dec 25, 2016

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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.

MAC302: Leveraging Amazon Machine Learning, Amazon Redshift, and an Amazon Simple Storage Service Data Lake for Strategic Advantage in Real Estate Dec 25, 2016

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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.

MBL201: AWS Mobile State of the Union - Serverless, New User Experiences, Auth, and More Dec 25, 2016

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AWS provides a range of services and tools to help you create industry leading, cloud-enabled mobile apps that can securely scale to millions of users globally. Join Amit Patel, GM of AWS Mobile, to hear our vision for mobile apps and the cloud, industry trends, recent product launches, and success stories directly from our customers. We'll walk through and demo the AWS Mobile offerings for building compelling cloud-enabled mobile apps and for engaging your app users. You’ll learn how to use these offerings (serverless – API Gateway/Lambda, Cognito, and new services) to make it easy to develop both your iOS and Android frontend, as well as your mobile backend.

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