AWS re:Invent 2018

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LFS301: Enabling Patient Centricity for Pfizer through AWS Cloud Dec 01, 2018

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Pfizer needed the ability to perform rapid analysis on its set of real-world evidence (RWE) data to improve patient outcomes, but its existing platform could not scale and meet its objectives. Pfizer collaborated with Deloitte to transform its real-world data and analytics capabilities that maximize insights and avoid duplicative investments by migrating their existing RWE data and analytics environment to the AWS Cloud. Learn how these strategies for planning, executing, and validating the success of these capabilities helped position Pfizer to use the AWS Cloud environment as the cornerstone of its patient-centric analytics to expand and incorporate new AI/ML capabilities, such as Amazon SageMaker. This session is brought to you by AWS partner, Deloitte Consulting LLP.

LFS302: Drug Discovery Innovation in a Precompetitive Cloud Platform Dec 01, 2018

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Informatics systems used by research scientists today have significant limitations, since they come from many vendors, use different data formats, and were developed with various UI standards. These limitations create barriers to accessing and integrating heterogeneous, siloed research data in a meaningful way to facilitate innovation and collaboration. In this session, Accenture and Merck discuss the expanded capabilities and benefits-for drug discovery organizations and software providers-of a newly launched research platform that gives the research science world a highly elastic, cloud infrastructure with a single UI and advanced computing power that accelerates drug discovery activities and enables competitive differentiation. This session is brought to you by AWS partner, Accenture.

LFS304: Building IoT Devices for Regulated Industries Dec 01, 2018

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In this session, learn how to use AWS IoT services to build devices that can be used in regulated industries, like healthcare and pharma manufacturing. Come hear from AWS solution architects about how you can use the AWS IoT Core to enable your devices, services like AWS Greengrass to build devices that have local compute, messaging, data caching, sync, and machine learning inference capabilities and AWS IoT Analytics to run sophisticated analytics on massive volumes of IoT data without having to worry about all the cost and complexity typically required to build your own IoT analytics platform. You will also hear about how you can set up the fine-grained access control, auditability, and automated guardrails necessary for the creation and maintenance of regulated workloads following Good Laboratory, Clinical, and Manufacturing Practices (GxP) and other industry standards and ISOs.

MAE201: What's New from AWS for M&E, & The Executive Perspective Dec 01, 2018

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In this wide-ranging keynote session, first hear from AWS VP Carla Stratfold on the major forces affecting the industry, then learn from AWS Global M&E Tech Lead Usman Shakeel about the latest and most exciting releases coming out of re:Invent relevant to the M&E industry. And finally, hear how technical leaders at the forefront of the industry are responding to accelerating changes in the media landscape.

MAE203: Hollywood's Cloud-Based Content Lakes: Modernized Media Archives Dec 01, 2018

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Content lake architecture can evolve the media workflow by providing efficiency from content security all the way to value-added services, such as machine learning and content monetization. In this session, technical leaders from 21st Century Fox, Warner Bros., and Astro Malaysia discuss the migration of their petabyte-scale video libraries (production and distribution archives) to the cloud in order to increase the customer reach and value of their media archives. Discover some of the lessons learned, the TCO analysis around various different storage tiers, the challenges and best practices from 10s of petabytes ingest, storage, and value-added compute at scale.

MAE204: Ticketek Sells 1,000s of Tickets a Minute with AWS Service Catalog Dec 01, 2018

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Learn how the world's third-largest ticketing company uses AWS Service Catalog to automate its entire PCI-compliant platform to better manage peak demand during major concert ticket sales for some of the world's largest venues, including the 100,000-seat Melbourne Cricket Ground in Australia. In this session, Deloitte's Zack Levy and Ticketek CTO Matt Cudworth discuss taking automation to another level-from manually managing ‚Äòhot shows' to using AWS Service Catalog to automate multiple AWS services (Amazon EC2, Amazon Route 53, Amazon VPC, Amazon ELB, and AWS CloudFormation), enabling Ticketek to scale and run multiple hot shows concurrently across multiple jurisdictions. This session is brought to you by AWS partner, Deloitte Consulting LLP.

MAE305: Becoming a Platform Business: Lessons from the TV Industry Dec 01, 2018

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Join us as we describe the vision and possibilities for platform businesses, including an in-depth look at OpenAP, TV's first open platform for cross-publisher audience targeting. Open platforms are breaking down barriers, enabling companies to connect best-of-breed cloud services to solve problems, respond faster, and create a competitive advantage. The TV industry open platform, built by OpenAP and Accenture, is highly available, with end-to-end security and massive scalability for both advertisers and publishers. By building the application from the ground up and leveraging AWS services, the Accenture team released the product ahead of schedule-only five months from kickoff to launch. This session is brought to you by AWS partner, Accenture.

MAE306: Operationalizing Machine Learning to Deliver Content at Scale Dec 01, 2018

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The world's leading content creators, broadcasters, OTT providers, and distributors rely on Deluxe's experience and expertise to globally create, transform, localize, and distribute hundreds of thousands of assets per month. This scale presents a unique set of challenges and opportunities, further multiplied by the benefits that cloud orchestration, automation, and self-service bring to technical teams. In this session, we dive into how we are applying machine learning to workflows such as fingerprinting and conformance, and specifically how we are operationalizing these as a set of primitives that can be consumed by both customers and services alike. We share our rationale behind our selection of machine learning technologies for workflows such as supply chain, creative and visual effects, where they make sense, and we discuss how we make them available to internal development and data science teams through our MLOps/DevOps pipelines.

MFG201: Leadership Session: AWS Semiconductor Dec 01, 2018

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Semiconductor design companies, electronic design automation (EDA) vendors, and foundries remain competitive by innovating and reducing time to market. AWS is deeply invested in semiconductor use cases, including EDA, emulation, and smart manufacturing, including data lake and IoT/AI. We care about this because Amazon depends on faster semiconductor innovation from our suppliers and in our own silicon teams. We have a wide breadth of services that will directly benefit the entire industry. In this session, learn how to achieve the maximum possible performance and throughput from design and engineering workloads running on AWS. We demonstrate specific optimization techniques and share architectures to accelerate batch and interactive workloads on AWS. We also demonstrate how to extend and migrate on-premises, high performance compute workloads with AWS, and use a combination of On-Demand Instances, Reserved Instances, and Spot Instances to minimize costs. Learn how semiconductor customers address security as they move to the cloud as they discuss the AWS capabilities and controls available to secure sensitive design IP and offer strategies for data classification, management, and transfer to third parties.

MFG301: Optimize Smart Factories Using Data Lakes and Machine Learning on AWS Dec 01, 2018

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Manufacturing companies collect a large amount of process data, but common issues, such as disparate data sources, stranded data, and ownership, make it difficult to identify insights. In this session, learn how to build a data lake on AWS using services such as Amazon EC2, Amazon S3, AWS Lambda, and IAM. Also, we review a reference architecture supporting data ingestion, event rules, analytics, and the use of machine learning (ML) for manufacturing analytics. We also discuss how a large printer manufacturer created a data lake by combining streaming manufacturing plant data with batch data from their SAP system and suppliers to create a single source of truth.

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