AWS re:Invent 2017

Latest Episodes:

MCL251: An Eye in the Sky: How Radiant Solutions Processes Satellite Imagery with AI and Amazon Mechanical Turk Dec 01, 2017

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This planet is a big place filled with amazing and unusual things. Understanding every object, location, and action on this pale blue dot is an enormous challenge. As the world's leading provider of high-resolution Earth imagery, data, and analysis, DigitalGlobe faces this challenge every day. They use automatic computer vision and machine learning where possible, but so far, the only true solution requires the most powerful information processing machine we know: the human brain. Scaling this solution to work on the trillions of satellite pixels collected by DigitalGlobe every day requires thousands of brains, all working in harmony. To address this, DigitalGlobe | Radiant's (now Radiant Solutions) Tomnod service uses Amazon Mechanical Turk, a crowdsourcing internet platform, to identify small objects appearing in large areas of new satellite imagery. Tomnod is heavily used for commercial and humanitarian purposes. In this session, you hear how Radiant Solutions uses crowdsourcing to help solve large-scale computer vision and machine learning problems.

MCL301: Building a Voice-Enabled Customer Service Chatbot Using Amazon Lex and Amazon Polly Dec 01, 2017

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Amazon Lex is a service for building conversational interfaces into any application using voice and text, and Amazon Polly is a service that turns text into lifelike speech. This session combines both of these AWS services during which the presenter will demonstrate how to build a Help Desk chatbot that feature spoken-voice interfaces. Attendees will be provided with the foundational skills for those looking to enrich their applications with natural, conversational interfaces. Liberty Mutual Insurance will also present on their chat platform architecture to demonstrated how they areusing Amazon Lex in their organization as an employee digital assistant.

MCL302: Maximizing the Customer Experience with AI on AWS Dec 01, 2017

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In this session, you will learn best practices for implementing simple to advanced AI/ML use cases on AWS. First. we will review the decision points for using democratised services such as Amazon Lex, Amazon Polly and integration with services such as Amazon Connect. Then we will look at real use cases, optimising the customer experience with chatbots, streamlining the customer experience predicting responses with Amazon Connect. Finally, we will dive deep into the most common of these patterns and cover design and implementation considerations. By the end of the session you will understand how to use Amazon Lex to optimise the user experience, through different user interactions.

MCL303: Deep Learning with Apache MXNet and Gluon Dec 01, 2017

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Developing deep learning applications just got even simpler and faster. In this session, you will learn how to program deep learning models using Gluon, the new intuitive, dynamic programming interface available for the Apache MXNet open-source framework. We'll also explore neural network architectures such as multi-layer perceptrons, convolutional neural networks (CNNs) and LSTMs.

MCL305: Scaling Convolutional Neural Networks with Kubernetes and TensorFlow on AWS Dec 01, 2017

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In this session, Reza Zadeh, CEO of Matroid, presents a Kubernetes deployment on Amazon Web Services that provides customized computer vision to a large number of users. Reza offers an overview of Matroid's pipeline and demonstrates how to customize computer vision neural network models in the browser, followed by building, training, and visualizing TensorFlow models, which are provided at scale to monitor video streams.

MCL306: Making IoT Devices Smarter with Amazon Rekognition Dec 01, 2017

Show notes

Motion detection triggers have reduced the amount of video recorded by modern devices. But maybe you want to reduce that further—maybe you only care if a car or a person is on-camera before recording or sending a notification. Security cameras and smart doorbells can use Amazon Rekognition to reduce the number of false alarms. Learn how device makers and home enthusiasts are building their own smart layers of person and car detection to reduce false alarms and limit video volume. Learn too how you can use face detection and recognition to notify you when a friend has arrived.

MCL307: Amazon Polly Tips and Tricks: How to Bring Your Text-to-Speech Voices to Life Dec 01, 2017

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Although there are many ways to optimize the speech generated by Amazon Polly's text-to-speech voices, you might find it challenging to apply the most effective enhancements in each situation. Learn how you can control pronunciation, intonation, and timing for text-to-speech voices. In this session, you get a comprehensive overview of the available tools and methods available for modifying Amazon Polly speech output, including SSML tags, lexicons, and punctuation. You also get recommendations for streamlining application of these techniques. Come away with insider tips on the best speech optimization techniques to provide a more natural voice experience.

MCL308: Using a Digital Assistant in the Enterprise for Business Productivity Dec 01, 2017

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Enterprises must transform at the pace of technology. Through chatbots built with Amazon Lex, enterprises are improving business productivity, reducing execution time, and taking advantage of efficiency savings for common operational requests. These include inventory management, human resources requests, self-service analytics, and even the onboarding of new employees. In this session, learn how Infor integrated Amazon Lex into their standard technology stack, with several use cases based on advisory, assistant, and automation roles deeply rooted in their expanding AI strategy. This strategy powers one of the major functionalities of Infor Coleman to enable their users to make business decisions more quickly.

MCL312: Building Multichannel Conversational Interfaces Using Amazon Lex Dec 01, 2017

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In this session, discover how to build a multichannel conversational interface that leverages a preprocessing layer in front of Amazon Lex. This preprocessing layer can enable customers to integrate their conversational interface with external services and use multiple specialized Amazon Lex chatbots as part of an overall solution. As an example of how to integrate with an external service, learn how to integrate with Skype. Watch it in action through a chatbot demonstration with interaction through Skype messaging and voice.

MCL313: Deep Learning Using Caffe2 on AWS Dec 01, 2017

Show notes

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.

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