AWS re:Invent 2017

Join Facebook's Pieter Noordhuis to learn about Caffe2, a lightweight and scalable framework for deep learning. You'll learn about its features, the way Facebook applies it in production, and how to use Caffe2 to create and train your own deep learning models on Amazon EC2 P3 instances, which use the latest NVIDIA Volta architecture for GPU-acceleration. This session will also discuss the cost tradeoffs and time to model measurements for deep learning.

Companies can have large amounts of image and video content in storage with little or no insight about what they have—effectively sitting on an untapped licensing and advertising goldmine. Learn how media companies are using Amazon Rekognition APIs for object or scene detection, facial analysis, facial recognition, or celebrity recognition to automatically generate metadata for images to provide new licensing and advertising revenue opportunities. Understand how to use Amazon Rekognition APIs to index faces into a collection at high scale, filter frames from a video source for processing, perform face matches that populate a person index in ElasticSearch, and use the Amazon Rekognition celebrity match feature to optimize the process for faster time to market and more accurate results.

Reinforcement learning is emerging as a powerful tool for autonomous driving, enabling complex maneuvers in a wide range of traffic situations. This session demonstrates how to build a reinforcement learning engine for autonomous vehicles on AWS, showing how it receives environmental input from object detection and produces outputs for controlling the vehicle's steering, acceleration, and braking.

Deep learning and IoT are emerging as an innovative pairing due to the explosion of data produced by a growing number of devices. The data this is generating needs to be quickly analyzed to produce meaningful insights and take action. In this session, we discuss how deep learning can be applied to real-world IoT use cases with a demo of computer vision and anomaly detection. We also do a step-by-step tutorial on how to develop deep learning models for computer vision at the edge using NVIDIA Jetson.

At Netflix, we use machine learning (ML) algorithms extensively to recommend relevant titles to our 100+ million members based on their tastes. Everything on the member home page is an evidence-driven, A/B-tested experience that we roll out backed by ML models. These models are trained using Meson, our workflow orchestration system. Meson distinguishes itself from other workflow engines by handling more sophisticated execution graphs, such as loops and parameterized fan-outs. Meson can schedule Spark jobs, Docker containers, bash scripts, gists of Scala code, and more. Meson also provides a rich visual interface for monitoring active workflows and inspecting execution logs. It has a powerful Scala DSL for authoring workflows as well as the REST API. In this session, we focus on how Meson trains recommendation ML models in production, and how we have re-architected it to scale up for a growing need of broad ETL applications within Netflix. As a driver for this change, we have had to evolve the persistence layer for Meson. We talk about how we migrated from Cassandra to Amazon RDS backed by Amazon Aurora.

Join us for a deep dive on how to use Amazon Rekognition for real world image analysis. Learn how to integrate Amazon Rekognition with other AWS services to make your image libraries searchable. Also learn how to verify user identities by comparing their live image with a reference image, and estimate the satisfaction and sentiment of your customers. We also share best practices around fine-tuning and optimizing your Amazon Rekognition usage and refer to AWS CloudFormation templates.

How do you create a frictionless travel experience, where merely by walking into the hotel you're automatically checked in and hotel associates greet you by name? By combining sophisticated Machine Learning, Location/Motion Tracking, Event Streaming and Serverless architecture with AWS components run on Cloud, we're able to create the foundation for this experience without significant time and capital investment. Accenture will demonstrate how to determine accurate location and motion by passively scanning Bluetooth signals, establish who and where you are, and deduce your intent by creating a network of information including profile, motion and activity. We are reinventing the hospitality experience, and are beginning to use the technology in industries as diverse as healthcare and mining. Join Accenture for a demo and architecture discussion focused on the power of combining architecture components to optimize the customer experience, speed to market, and operating cost. Attendees will learn more about the considerations, risks and implications of the company's cloud transformation program; see examples of reference architectures and implementation guides; and understand what contributed to the success of the program.The patterns presented will be broadly applicable to complex, global organizations with aspirations to make the journey to AWS cloud Session sponsored by Accenture

In this session. We will provide an overview of the latest Amazon Rekognition features including real-time face recognition, Text in Image recognition, and improved face detection. Amazon Rekognition recently added three new features: detection and recognition of text in images; real-time face recognition across tens of millions of faces; and detection of up to 100 faces in challenging crowded photos. In this session, we will cover features, benefits and use cases for these latest Rekognition features, while highlighting customer examples and a brief demo showcasing Amazon Rekognition.

Tensors are higher order extensions of matrices that can incorporate multiple modalities and encode higher order relationships in data. This session will present recently developed tensor algorithms for topic modeling and deep learning with vastly improved performance over existing methods. Topic models enable automated categorization of large document corpora, without requiring labeled data for training. They go beyond simple clustering since they allow for documents to have multiple topics. Tensor methods provide a fast and a guaranteed method for training these models. They incorporate co-occurrence statistics of triplets of words in documents. We are releasing a fast and a robust implementation that vastly outperform existing solutions while providing significantly faster training times and better topic quality. Moreover, training and inference are decoupled in our algorithm, so the user can select the relevant part based on their requirements.  We will present benchmarks across multiple datasets of different sizes and AWS instance types, and provide notebook examples.

During this session, we will provide an overview of Amazon Rekognition Video, a deep learning powered video analysis service that tracks people, detects activities, and recognizes objects, celebrities, and inappropriate content. Amazon Rekognition Video can detect and recognize faces in live streams. Rekognition Video also analyzes existing video stored in Amazon S3 and returns specific labels of activities, people and faces, and objects with time stamps so you can easily locate the scene. For people and faces, it also returns the bounding box, which is the specific location of the person or face in the frame.  We will also cover different use cases for Amazon Rekognition Video in applications such as security and public safety, and media and entertainment.