AWS re:Invent 2018

Learn how Wellington Management, a global investment management firm that manages more than 1 trillion USD on behalf of its clients, is executing an all-in strategy to exit all of its physical data centers by 2019. The migration includes both commercial applications and a large number of custom-developed analytical, portfolio management, and trading applications. We share the lessons learned, both positive and constructive, by a team that has been on this journey for over five years. We also discuss usage of many key AWS services, including Amazon Virtual Private Cloud, AWS Direct Connect, Amazon EC2, Amazon ECS, AWS Lambda, Amazon Redshift, Amazon Relational Database Service, and others.

Customer demands for higher levels of service and value, constantly evolving technology capabilities, and stringent regulatory requirements are all powerful forces reshaping retail banking. Built exclusively on AWS, Starling Bank's 100% cloud-based, mobile-only banking solution satisfies regulators in terms of its resilience, security, and reliability. It also satisfies consumers by giving them greater control over their data, streamlining the account opening process, accelerating payments, and providing access to innovative new services developed from scratch with open APIs, a developer platform, integration with Apple Pay, Google Pay, and Fitbit Pay and a custom backend ledger and payments integrations. Starling Bank is leading the open banking revolution. In this session, learn how Starling Bank delivers value to their customers and innovates at a very fast pace in a sector that can be slow to evolve.

How can Financial Services companies meet growing customer demands for personalized, high-quality service while satisfying regulatory obligations? In this session, learn how Amazon Connect, a self-service, cloud-based contact center, can integrate with machine learning services on AWS such as Amazon Transcribe, Amazon Comprehend, and Amazon Lex to enable financial institutions to deliver transformational omni-channel experiences to their customers while complying with regulations like MiFID II, the GDPR, and the SEC's data retention rules. Learn how to use Amazon Connect to easily set up a cloud-based contact center solution that scales to support businesses of any size. Then learn how to integrate Amazon Connect with machine learning services on AWS to make contact center content available for search and analysis by natural language processing tools, which can yield valuable insights into customer sentiment, customer preferences, and the most common issues customers raise during service interactions. Complete Title: AWS re:Invent 2018: Financial Svcs: Build Customer-Centric Contact Centers with Amazon Connect & Machine Learning (FSV301)

As financial institutions look to accelerate and scale their use of machine learning, they need to address questions related to specific results, such as the version of the code and the data that lead to a particular inference. The use of disparate and increasingly non-traditional data sources for activities such as targeted marketing, fraud detection, and improved returns is driving a need for structured development of machine learning models. In this session, we'll discuss how we can use a combination of AWS services including Amazon SageMaker, AWS CodeCommit, AWS CodeBuild, and AWS CodePipeline to create a workflow that will help financial institutions meet their requirements and drive business results.

Late in 2017, Mutual of Omaha began a cloud journey to modernize its legacy contact centers. Using Amazon Connect-supported by Amazon Lex, Amazon Polly, AWS Lambda, and Kibana, Accenture helped Mutual of Omaha improve customer engagement, developed self-service features using leading-edge speech recognition, and developed powerful analytics to continuously drive positive change. Mutual of Omaha plans to reduce TCO annually with Amazon Connect compared with its legacy solution. As of August 2018, three contact centers are live in Amazon Connect, with several more scheduled to go live in 2018. This session is brought to you by AWS partner, Accenture.

With over $5 trillion in assets under management, Vanguard requires a secure, flexible, and fast data and analytics platform. By deploying Tableau on AWS, Vanguard was able to move from their private cloud to the AWS Cloud, and significantly reduce the administrative workload of Vanguard's IT team, allowing them to focus on innovating. In this session, learn how mission-critical processes such as configuring deployments, adding nodes, and creating backups are automated via scripts. Expect to leave with a clear picture of how a global deployment of Tableau on AWS supports peak surges and avoids the sunk costs of on-premises hardware. This session is brought to you by AWS partner, Tableau Software.

Curious to understand how operating model changes are key to cloud adoption? Last year, Vanguard presented its journey to the cloud. CTO Jeff Dowds anticipated that there was a lot more work to do organizationally, especially around operating models. In this session, Jeff discusses the lessons learned from implementing new organizational structures tailored towards delivering business value in the cloud. You have an opportunity to learn how to optimize and run cloud operations optimally and efficiently. One of the core tenants of DevOps is continuous improvement and continuous learning. As enterprises mature in their cloud journey, their focus shift from 'how do we build in the cloud' to 'how do we optimize for the cloud'. Vanguard discusses its journey into the adoption of chaos engineering in a highly regulated environment where testing in production is typically not allowed. This session is brought to you by AWS partner, Deloitte Consulting LLP.

In this session, learn how Supercell architected its analytics pipeline on AWS. We dive deep into how Supercell leverages Amazon Elastic Compute Cloud (Amazon EC2), Amazon Kinesis, Amazon Simple Storage Service (Amazon S3), Amazon EMR, and Spark to ingest, process, store, and query petabytes of data. We also dive deep into how Supercell's games are architected to accommodate scaling and failure recovery. We explain how Supercell's teams are organized into small and independent cells and how this affects the technology choices they make to produce value and agility in the development process.

Understanding gamer behavior is critical in acquiring, retaining, and monetizing users effectively. The cycle requires constant fine-tuning through expensive and complex live operations to offer fresh, fun, and challenging experiences. In this session, learn how Rovio uses machine learning (ML) to make this process faster, more efficient, and more accurate. Learn how to leverage AWS compute, analytics, and database services, such as Amazon S3, Amazon EC2, Amazon EMR, Amazon Athena, Amazon DynamoDB, and Amazon Redshift, to build an ML workflow that predicts the future interests and behaviors of gamers to better serve your needs and theirs.

In this session, we provide an update on the AWS China Region, and we discuss the business and technical best practices occurring there. Whether you're new to the AWS China Region or you have familiarity with it, you will find useful information. For those who are new, learn how to register an AWS China account, complete China's ICP filing, optimize network performance between China and the globe, and more. For those who already have experience with the AWS China Region, learn about the new services launched there in the past year.