AWS re:Invent 2018

In this session, hear from an AWS customer about how they leveraged Amazon Rekognition deep learning-based image and video analysis to power a data-driven decision system for creative asset production. Learn how this customer was able to leverage the raw data provided by Amazon Rekognition combined with performance data to discover actionable insights. See a demonstration of the solution, and hear about media- and advertising-specific use cases. Learn from the customer's experiences implementing their architecture, the challenges, and the pleasant surprises along the way.

Amazon has a long history in AI, from personalization and recommendation engines to robotics in fulfillment centers. Amazon Go, Amazon Alexa, and Amazon Prime Air are also examples. In this session, learn more about the latest machine learning services from AWS, and hear from customers who are partnering with AWS for innovative AI.

Curious about how Amazon machine learning (ML) services can enable healthcare organizations to find the insights they need to survive and thrive? Join us to learn how Takeda researchers built and trained their own disease-specific ML models, including deep-learning models using Deloitte ConvergeHEALTH running on AWS to simulate and quantify the overall disease burden and identify potential risks. This session is brought to you by AWS partner, Deloitte Consulting LLP.

A single device can produce thousands of events every second. In traditional implementations, all data is transmitted back to a server or gateway for scoring by a machine learning (ML) model. This data is also stored in a data repository for later use by data scientists. In this session, we explore data science techniques for dealing with time series data leveraging Amazon SageMaker. We also look at modeling applications using deterministic rules with streaming pipelines for data prep, and model inferencing using deep learning frameworks directly onto edge devices or onto AWS Lambda using Project Flogo, an open-source event-driven framework. This session is brought to you by AWS partner, TIBCO Software Inc.

In this session, learn how the C3 Platform on AWS is architected and why it accelerates the development of enterprise-scale AI applications. Hear how customers like the US Air Force, Enel, and global manufacturing leaders are using C3 on AWS to rapidly aggregate, unify, federate, and normalize data from sensor networks and enterprise IT systems, and apply ML/AI algorithms against this data to unlock significant economic value. Hear from global organizations that are solving complex business challenges, from optimizing the supply network, to predicting which assets will fail, to identifying fraud and money laundering. This session is brought to you by AWS partner, C3.

In this session, learn how the C3 Platform on AWS is architected to accelerate the development of modern AI applications. Hear how customers and partners have used the C3 Type System's data-object centric abstraction layer to realize 10-100x productivity gains when building complex AI/ML applications. In addition, hear how global organizations are using C3 on AWS to solve complex business challenges, from optimizing the supply network, to predicting asset failure, to identifying fraud and money laundering. This presentation is brought to you by AWS partner, C3.

Artificial intelligence (AI) is rapidly evolving, and much of the advancement is driven by deep learning, a machine learning technique inspired by the inner workings of the human brain. In this session, learn what deep learning is and how you can use it in your applications to unlock new and exciting capabilities for your customers and business. Also hear from Samsung SDS about how it developed a deep-learning model for cardiac arrhythmia detection using Apache MXNet, an open-source deep-learning framework. By the end of the session, you will understand how to leverage deep learning in your applications and get started with it.

Video-based tools have enabled advancements in computer vision, such as in-vehicle use cases for AI. However, it is not always possible to send this data to the cloud to be processed. In this session, learn how to train machine learning models using Amazon SageMaker and deploy them to an edge device using AWS Greengrass, enabling you process data quickly at the edge, even when there is no connectivity.

Amazon brings natural language processing, automatic speech recognition, text-to-speech services, and neural machine translation technologies within the reach of every developers. In this session, learn how to add intelligence to any application with machine learning services that provide language and chatbot functions. See how others are defining and building the next generation of apps that can hear, speak, understand, and interact with the world around us.

Analyzing customer service interactions across channels provides a complete 360-degree view of customers. By capturing all interactions, you can better identify the root cause of issues and improve first-call resolution and customer satisfaction. In this session, learn how to integrate Amazon Connect and AWS machine learning services, such Amazon Lex, Amazon Transcribe, and Amazon Comprehend, to quickly process and analyze thousands of customer conversations and gain valuable insights. With speech and text analytics, you can pick up on emerging service-related trends before they get escalated or identify and address a potential widespread problem at its inception.

Join us for a deep dive on the latest features of Amazon Rekognition. Learn how to easily add intelligent image and video analysis to applications in order to automate manual workflows, enhance creativity, and provide more personalized customer experiences. We share best practices for fine-tuning and optimizing Amazon Rekognition for a variety of use cases, including moderating content, creating searchable content libraries, and integrating secondary authentication into existing applications.

Based on the same technology used at Amazon.com, Amazon Forecast uses machine learning to combine time series data with additional variables to build forecasts. Amazon Forecast requires no machine learning experience to get started. You only need to provide historical data, plus any additional data that you believe may impact your forecasts. Come learn more.

Amazon Mechanical Turk operates a marketplace for crowdsourcing, and developers can build human intelligence directly into their applications through a simple API. With access to a diverse, on-demand workforce, companies can leverage the power of the crowd for a range of tasks, from ML training and automating manual tasks to generating human insights. In this session, we cover key concepts for Mechanical Turk, and we share best practices for how to integrate and scale your crowdsourced application. By the end of this session, expect to have a general understanding of Mechanical Turk and know how to get started harnessing the power of the crowd.

While technology continues to improve, there are still many things that human beings can do much more effectively than computers, such as performing data deduplication or content moderation. Traditionally, such tasks have been accomplished by hiring a large temporary workforce-which is time consuming, expensive, and difficult to scale-or have gone undone. However, businesses or developers can use Amazon Mechanical Turk (Mechanical Turk) to access thousands of on-demand workers-and then integrate the results of that work directly into their business processes and systems. In this session, learn how enterprises are using Mechanical Turk to scale and automate their human-powered workflow.

Amazon Textract enables you to easily extract text and data from virtually any document. Today, companies process millions of documents by manually entering the data or using customized optical character recognition solutions, which are prone to error and consume valuable resources. Join us to learn how Amazon Textract uses machine learning to simplify document processing by enabling fast and accurate text and data extraction so you can process millions of documents in hours.