Leading companies are using machine learning (ML) to power innovation across industries, including healthcare, automotive, and finance. In this session, learn how to build scalable ML solutions using the Amazon SageMaker platform, as well as our services for computer vision, language, and analytics. We also demonstrate real-world use cases for enterprises to get more value from their data and integrate and manage intelligent systems and processes.
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.
Deep learning has the potential to enable extremely advanced AI applications. But it's not taught in most computer science programs, and you may have a lot of questions. In this session, understand how deep learning works, and learn key concepts such as neural networks, activation functions, and optimizers. We show you how deep learning models improve through complex pattern recognition in pictures, text, sounds, and other data to produce more accurate insights and predictions. We also share examples of common deep learning use cases, such as computer vision and recommendation models. Finally, we help you understand how to get started using popular deep learning frameworks, such as TensorFlow, Apache MXNet, and PyTorch.
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 use machine learning to quickly process and analyze thousands of customer conversations to 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.
Customers are using automatic metadata extraction to fuel new insights and provide innovative services to their customers. In this session, we walk through the basic architecture patterns for implementing automatic metadata extraction using Amazon Rekognition, Amazon Transcribe, and Amazon Comprehend. We also share how to get started with the pre-configured AWS Media Analysis Solution.
In this session, you will learn how to build and deploy computer vision models using the AWS DeepLens deep-learning-enabled video camera and Amazon SageMaker.
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.
In this session, get general help on how to build or extend a project with AWS DeepLens.
Implementing natural language processing (NLP) models just got simpler and faster. In this chalk talk, learn how to implement NLP models using MXNet and the Gluon NLP toolkit, which provides implementations of state-of-the-art deep learning algorithms in NLP to help engineers, researchers, and students quickly prototype products, validate new ideas, and learn natural language processing.
In the novel, “The Hitchhiker's Guide to the Galaxy,” Douglas Adams described a Babel fish as a “small, yellow, and leech-like” device that you stick in your ear. In Star Trek, it is known simply as the universal language translator. Whatever you call it, there is no doubting the practical value of a device that is capable of translating any language into another. In this workshop, learn how to build a babel fish mobile app that recognizes voice and converts it to text (speech-to-text), translates the text to a language of your choice, and converts translated text to synthesized speech (text-to-speech).
A recent poll showed that 44% of customers would rather talk to a chatbot than a to human for customer support. In this workshop, we show you how to build a QnA bot (question and answer bot) that uses Amazon Lex and Alexa to provide a conversational interface for your customers to ask questions and get relevant answers quickly. We also show you how to use Amazon Elasticsearch Service on the backend to find the best matches to answer questions.
In this workshop, you step into the role of a startup that has assumed the challenge of providing a new type of EDM music festival experience. Your goal is to use machine learning (ML) and IoT to develop a connected fan experience that enhances the festival. Come and get hands-on experience with Amazon SageMaker with Intel C5 instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, and AWS Lambda as you build and deploy an ML model and then run inference on it from the cloud and from edge devices.
If you're new to deep learning, this workshop is for you. Learn how to build and deploy computer vision models using the AWS DeepLens deep learning-enabled video camera. Also learn to build a machine learning application and a model from scratch using Amazon SageMaker. Finally, learn to extend that model to Amazon SageMaker to build an end-to-end AI application.
While many ETL tools can handle structured data, very few can reliably process unstructured data and documents. In this session, learn how you can use Amazon Comprehend to address unstructured data extraction and transformation at scale so that key information can be extracted and used downstream for data integration and analytics.
Many of today's text-to-speech systems limit your choices to a few voices. If these voices aren't right for your needs, the process of adding more voices is usually costly and time consuming. Learn how you can use Amazon Polly for speech production and to modify voices for a range of uses, from game development and telephony to web publishing.
Learn how to build a multichannel conversational interface that uses a preprocessing layer in front of Amazon Lex, or route messages to other specialized bots. Amazon Lex offers built-in integrations with Slack, Twilio, Marketo, Salesforce, QuickBooks, Microsoft Dynamics, Zendesk, and HubSpot. Also learn how to integrate with any other application by combining the Amazon Lex API and Amazon API Gateway to integrate your Amazon Lex bot with any messaging service.
Amazon Transcribe is an automatic speech recognition service that makes it easy for developers to add speech-to-text capability to their applications. Join us and learn how you can build machine transcription into your existing workflows, such as transcribing videos for search engines or captioning.
Faced with higher volumes of content that require translation and with shorter completion times, more organizations are weighing the pros and cons of machine translation (MT) as a viable solution to tackle time-critical projects. Come to this session and learn how to adapt MT for your web localization workflows using best practices, such as caching for cost savings, custom glossary for improved quality, and smart A/B testing to evaluate quality.
Amazon brings natural language processing, automatic speech recognition, text-to-speech, and neural machine translation technologies within reach of every developer. Come to this session and learn how to build smarter applications or automate workflows with AWS language services.
Join us for a deep dive on building a searchable image library using Amazon Rekognition. We walk though creating a search index for objects and scenes so you can quickly retrieve images using labels created from automatic metadata extraction. Also learn how to use AWS Lambda to automatically maintain your image library.
In this session, learn how to use Amazon Rekognition Video with Amazon Kinesis to receive and process video streams. We walk through recognizing faces, giving access to Amazon Kinesis Data Streams, as well as starting and reading the streaming video analysis. Whether you are building a "Who's Who" app similar to the royal wedding, or you need to identify athletes in real time, it is simple to add intelligent video analysis to your live streams using Amazon Rekognition.
In this session, learn how to use the range of built-in, high-performance machine learning algorithms that come with Amazon SageMaker.
In this session, learn how to use TensorFlow in the Amazon SageMaker machine learning platform.
In this session, learn how to use Apache MXNet in the Amazon SageMaker machine learning platform.
In this session, learn how to use PyTorch in the Amazon SageMaker machine learning platform.
In this session, learn how to use Chainer, an open-source deep learning framework written in Python, in the Amazon SageMaker machine learning platform.
In this session, learn how to integrate custom algorithms into the Amazon SageMaker platform.
In this session, learn how to use your own framework in a Docker container within the Amazon SageMaker platform.
Machine learning (ML) can help people with disabilities by using facial and object recognition, text-to-speech, automatic translation, and transcription to create assistive applications. In this chalk talk, learn how to assemble ML APIs from AWS to help people in new ways.
How do you use machine learning with data that isn't labeled? The unsupervised learning capabilities of Amazon SageMaker can easily handle unstructured data. In this chalk talk, we discuss the intricacies of unsupervised algorithms that are built into Amazon SageMaker, including clustering with k-means and anomaly detection with Random Cut Forest.
There are unique challenges to building highly accurate models that detect small objects in aerial and overhead imagery. In this chalk talk, we dive deep into using convolutional neural networks (CNNs) with Amazon SageMaker in order to build and train aerial object detection models. We build advanced models using AWS public datasets, such as SpaceNet and LandSat, as we work with DigitalGlobe's GBDX Notebooks.
XGBoost makes applying machine learning (ML) to real-world scenarios easy and powerful. Amazon SageMaker has XGBoost built in, and this enables the transition of ML models from training to production at scale. In this chalk talk, we discuss the details of using XGBoost on Amazon SageMaker, and we cover how to train and deploy ML models in a way that is simple, powerful, and scalable.
Machine learning provides a new way of looking at sports analytics and experiencing sporting events. Custom-built models can now be used to provide live sports analytics. In this chalk talk, learn how to train models that can detect sports events, such as a substitutions, goals, and red cards, using services like Amazon Rekognition and Amazon Transcribe.
Automatic video transcription and translation can help make videos more available and accessible to a global audience in many languages, enabling your employees or customers to access, understand, and benefit from your content. In this chalk talk, we discuss how to transcribe videos, translate them in the required languages in a multilingual application, and enable video search in the viewer’s preferred language—all in an automated and cost-effective manner.
The demand for chat apps is growing wildly—from social apps and business collaboration to in-game chat and customer support. Historically, building a scalable, feature-rich chat app was surprisingly difficult. In the chalk talk, we discuss how to build intelligent, scalable chat apps for mobile and web use with Amazon Translate, Amazon Comprehend, and Amazon Polly.
As companies face higher volumes of content that require translation, and as the time allotted for these projects shrinks, more and more organizations are weighing the pros and cons of machine translation as a viable solution to tackle time-critical projects. In this chalk talk, we discuss the ways you can adapt machine translation for your web localization workflows.
Sophisticated AI capabilities can help us manage the exploding number of information sources and tools required to perform our daily tasks. In this chalk talk, we describe how intelligent agents can be designed to quickly and efficiently complete tasks delegated by users. To build this intelligent agent, we combine a number of AWS services, such as Amazon Polly, Amazon Lex, Amazon Rekognition, Amazon Sumerian, and Amazon ElastiCache along with other technologies, such as CLIPS and Reinforcement Learning. Come hear us discuss the project’s architecture, implementation, and demo progress made to date.
Visual search engines have a growing importance at companies like Pinterest as well as at e-commerce companies like Amazon.com and Gilt. In this chalk talk, we show you how to build a visual search engine using Amazon SageMaker and AWS Fargate.
Companies have ever-growing media libraries, making them increasingly difficult to index and search. In this session, we describe how to maintain your library by using Amazon Rekognition, Amazon Transcribe, and Amazon Comprehend to perform automatic metadata extraction from image, video, and audio files. We show you how to then use this metadata to build a serverless media library that can be filtered by image tags, celebrities, and more.
In the world of sports entertainment, the fast pace of live events makes it difficult to keep up with new records and highlights that occur during games. In this session, learn how machine learning can combine internal statistics feeds with image player recognition to log when new player records are set. See how this solution uses Amazon Rekognition to identify the player, AWS Lambda to determine if the play is a new record for the particular athlete, and then automatically creates an image to share on social media that highlights the player in action.
In healthcare, predicting adverse events is the key to improving patient care. To achieve this, machine learning models need to be built and trained, and this requires integrating large datasets. Sources of data include claims data, diagnosis codes, procedure codes, medication, and patient billing from millions of patient records. In this chalk talk, we describe how to use Amazon SageMaker for large datasets in order to predict adverse health events.
Researchers from the University of Michigan and Georgia Tech, in collaboration with the AWS Research Initiative, have developed new techniques to identify financial market manipulation in high-volume, high-velocity market data streams. They are using a combination of data-driven and model-based techniques to identify financial market manipulation. In this session, we discuss the use of machine learning using Amazon SageMaker to study, process, and analyze huge volumes of data to prevent financial market manipulation.
Providing multilingual content represents a great opportunity for site owners. Although English is the dominant language of the web, native English speakers comprise only 26% of the total online audience. In this chalk talk, we discuss how you can make your web content more accessible with text-to-speech and machine translation. By offering written and audio versions of your content in multiple languages, you can meet the needs of a larger international audience.
When indexing large amounts of media files, it can become difficult to search through them to find certain objects and individuals. In this session, we show you how creating a custom celebrity list enables you to index your media files by the people you train it to recognize. Come and see how this solution can serve as the foundation for creating automated sports highlight reels, building face-based user verification systems, and more.
Amazon SageMaker enables you to bring your existing Apache MXNet or TensorFlow script for your machine learning models. In this session, we walk through the details of bringing your own script for training your models at scale. We also go into detail on using local containers for repeated experiments for ease of use and scalability.
The TensorFlow deep learning framework is used for developing diverse AI applications including computer vision, natural language, speech, and translation. In this session, learn how to use TensorFlow within the Amazon SageMaker machine learning platform. This code-level session also includes tutorials and examples using TensorFlow.
Deep learning can be used for diverse AI use cases, including computer vision and natural language. In this session, learn how to build and train convolutional neural networks using PyTorch within the Amazon SageMaker machine learning platform. This code-level session includes tutorials and examples using PyTorch.
Amazon SageMaker, our fully managed machine learning platform, comes with pre-built algorithms and popular deep learning frameworks. Amazon SageMaker also includes an Apache Spark library that you can use to easily train models from your Spark clusters. In this code-level session, we show you how to integrate your Apache Spark application with Amazon SageMaker. We also dive deep into starting training jobs from Spark, integrating training jobs in Spark pipelines, and more.