Content lake architecture can evolve the media workflow by providing efficiency from content security all the way to value-added services, such as machine learning and content monetization. In this session, technical leaders from 21st Century Fox, Warner Bros., and Astro Malaysia discuss the migration of their petabyte-scale video libraries (production and distribution archives) to the cloud in order to increase the customer reach and value of their media archives. Discover some of the lessons learned, the TCO analysis around various different storage tiers, the challenges and best practices from 10s of petabytes ingest, storage, and value-added compute at scale.
Leadership Session: Digital Advertising - Customer Learning & the Road Ahead ...Amazon Web Services
In this session, learn how experienced leaders in digital advertising respond to the rapid evolution and sophistication of the advertising market driven by innovation and groundbreaking technology. Our customers share real-world applications they've leveraged in the cloud and how they see the media landscape changing as adoption of AI in the space becomes more widespread. Learn about existing and upcoming advancements and how they affect digital transformation in the years to come. Come away with ideas on how you can apply these learnings to your technology stack.
Democratize Data Preparation for Analytics & Machine Learning A Hands-On Lab ...Amazon Web Services
Machine learning (ML) outcomes are only as good as the data they are built upon. Preparing data for ML is time consuming and cumbersome; “data wrangling” for analytics can consume over 80% of project effort. ML Wrangling Assistant, based on Trifacta running on AWS, streamlines ML applications so teams can focus on the work that matters—creating accurate predictions that improve products, services, and organizational efficiency. In this lab, we cover one of two data preparation use cases. Marketing Analytics analyzes web ads by cleaning and transforming ecommerce transactions in a relational table combined to a clickstream semi-structured log file. Cross-Sell Analytics explores, structures, standardizes, and combines multiple file types (CSV, JSON, Excel) to create a single, consistent view of customers. Final outputs are the categorical features and attributes to train, test, and validate the data sets required by Amazon SageMaker to perform ML modeling.
Capture Voice of Customer Insights with NLP & Analytics (AIM415-R1) - AWS re:...Amazon Web Services
Understanding your customers is easier today than ever before. Natural language capabilities can capture a wealth of information, such as user sentiment and conversational intent. This workshop teaches you how to build an analytics pipeline that includes natural language processing (NLP) to better understand how to improve the customer experience. Attendees learn how to use AWS services, including Amazon Comprehend and Amazon Transcribe, to process and perform analysis on customer data, such as contact center call recordings.
Transform the Modern Contact Center Using Machine Learning and Analytics (AIM...Amazon Web Services
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.
Sequence-to-Sequence Modeling with Apache MXNet, Sockeye, and Amazon SageMake...Amazon Web Services
In this session, we discuss the "encoder-decoder architecture with attention," a state-of-the-art architecture for natural language processing. This architecture is implemented in the Sockeye package of MXNet and is used by the sequence-to-sequence algorithm of Amazon SageMaker.
Unlock the Full Potential of Your Media Assets, ft. Fox Entertainment Group (...Amazon Web Services
Machine learning (ML) enables developers to build scalable solutions that maximizes the use of media assets through automatic metadata extraction. From automatic transcription and language translation to face detection and celebrity recognition, ML enables you to automate manual workflows and optimize the use of your video content. In this session, learn how to use services such as Amazon Rekognition, Amazon Translate, and Amazon Comprehend to build a searchable video library, automate the creation of highlight reels, and more.
Deep Learning Applications Using PyTorch, Featuring Facebook (AIM402-R) - AWS...Amazon Web Services
With support for PyTorch 1.0 on Amazon SageMaker, you now have a flexible deep learning framework combined with a fully managed machine learning platform to transition seamlessly from research prototyping to production deployment. In this session, learn how to develop with PyTorch 1.0 within Amazon SageMaker using a novel generative adversarial network (GAN) tutorial. Then, hear from Facebook on how you can use the FAIRSeq modeling toolkit, which serves 6B translations daily for Facebook users, to train your own custom PyTorch models on Amazon SageMaker. Facebook also discusses the evolution of PyTorch 1.0 and features introduced to accelerate research and deployment.
Machine Learning for Improving Disaster Management and Response (WPS313) - AW...Amazon Web Services
In this session, we start with Twitter data from a past disaster and use Amazon Comprehend to discover insights and relationships. Finally, we use Amazon SNS to notify iOS, Android, and Fire OS-based mobile devices.
Leadership Session: Digital Advertising - Customer Learning & the Road Ahead ...Amazon Web Services
In this session, learn how experienced leaders in digital advertising respond to the rapid evolution and sophistication of the advertising market driven by innovation and groundbreaking technology. Our customers share real-world applications they've leveraged in the cloud and how they see the media landscape changing as adoption of AI in the space becomes more widespread. Learn about existing and upcoming advancements and how they affect digital transformation in the years to come. Come away with ideas on how you can apply these learnings to your technology stack.
Democratize Data Preparation for Analytics & Machine Learning A Hands-On Lab ...Amazon Web Services
Machine learning (ML) outcomes are only as good as the data they are built upon. Preparing data for ML is time consuming and cumbersome; “data wrangling” for analytics can consume over 80% of project effort. ML Wrangling Assistant, based on Trifacta running on AWS, streamlines ML applications so teams can focus on the work that matters—creating accurate predictions that improve products, services, and organizational efficiency. In this lab, we cover one of two data preparation use cases. Marketing Analytics analyzes web ads by cleaning and transforming ecommerce transactions in a relational table combined to a clickstream semi-structured log file. Cross-Sell Analytics explores, structures, standardizes, and combines multiple file types (CSV, JSON, Excel) to create a single, consistent view of customers. Final outputs are the categorical features and attributes to train, test, and validate the data sets required by Amazon SageMaker to perform ML modeling.
Capture Voice of Customer Insights with NLP & Analytics (AIM415-R1) - AWS re:...Amazon Web Services
Understanding your customers is easier today than ever before. Natural language capabilities can capture a wealth of information, such as user sentiment and conversational intent. This workshop teaches you how to build an analytics pipeline that includes natural language processing (NLP) to better understand how to improve the customer experience. Attendees learn how to use AWS services, including Amazon Comprehend and Amazon Transcribe, to process and perform analysis on customer data, such as contact center call recordings.
Transform the Modern Contact Center Using Machine Learning and Analytics (AIM...Amazon Web Services
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.
Sequence-to-Sequence Modeling with Apache MXNet, Sockeye, and Amazon SageMake...Amazon Web Services
In this session, we discuss the "encoder-decoder architecture with attention," a state-of-the-art architecture for natural language processing. This architecture is implemented in the Sockeye package of MXNet and is used by the sequence-to-sequence algorithm of Amazon SageMaker.
Unlock the Full Potential of Your Media Assets, ft. Fox Entertainment Group (...Amazon Web Services
Machine learning (ML) enables developers to build scalable solutions that maximizes the use of media assets through automatic metadata extraction. From automatic transcription and language translation to face detection and celebrity recognition, ML enables you to automate manual workflows and optimize the use of your video content. In this session, learn how to use services such as Amazon Rekognition, Amazon Translate, and Amazon Comprehend to build a searchable video library, automate the creation of highlight reels, and more.
Deep Learning Applications Using PyTorch, Featuring Facebook (AIM402-R) - AWS...Amazon Web Services
With support for PyTorch 1.0 on Amazon SageMaker, you now have a flexible deep learning framework combined with a fully managed machine learning platform to transition seamlessly from research prototyping to production deployment. In this session, learn how to develop with PyTorch 1.0 within Amazon SageMaker using a novel generative adversarial network (GAN) tutorial. Then, hear from Facebook on how you can use the FAIRSeq modeling toolkit, which serves 6B translations daily for Facebook users, to train your own custom PyTorch models on Amazon SageMaker. Facebook also discusses the evolution of PyTorch 1.0 and features introduced to accelerate research and deployment.
Machine Learning for Improving Disaster Management and Response (WPS313) - AW...Amazon Web Services
In this session, we start with Twitter data from a past disaster and use Amazon Comprehend to discover insights and relationships. Finally, we use Amazon SNS to notify iOS, Android, and Fire OS-based mobile devices.
Deep Dive on Amazon Rekognition, ft. Tinder & News UK (AIM307-R) - AWS re:Inv...Amazon Web Services
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.
M&E Leadership Session: The State of the Industry, What's New from AWS for M&...Amazon Web Services
In this wide-ranging keynote session, first hear from AWS VP Carla Stratfold on the major forces affecting the industry, then learn from AWS Global M&E Tech Lead Usman Shakeel about the latest and most exciting releases coming out of re:Invent relevant to the M&E industry. And finally, hear how technical leaders at the forefront of the industry are responding to accelerating changes in the media landscape.
Machine Learning at the Edge (AIM302) - AWS re:Invent 2018Amazon Web Services
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.
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.
Build Deep Learning Applications Using PyTorch and Amazon SageMaker (AIM432-R...Amazon Web Services
In this workshop, learn how to get started with the PyTorch deep learning framework using Amazon SageMaker, a fully managed platform to build, train, and deploy machine learning (ML) models at scale quickly and easily. First, we create a computer vision model using deep neural networks that helps us discover analytical information from our image dataset. Then, we use Amazon Redshift, a fully managed data warehouse, to perform analytics and find business value using the output of our ML model.
Provide Faster, Scalable Solutions to Support Research Use Cases with AWS (WP...Amazon Web Services
The Urban Institute chose a cloud-first strategy with AWS to increase the capacity and performance of big data and high-performance computing workloads for 300+ researchers. In this chalk talk, we demonstrate how our team piloted the execution of one of our signature microsimulation models in parallel, designing a new architecture in the cloud that required minimal changes to the original model’s source code. We also show how our team worked directly with AWS to develop open source code that launches powerful Amazon Elastic MapReduce (Amazon EMR) Spark clusters with minimal setup time, and combined it with our Elastic Cloud Computing Environment to allow for a simple Spark user experience.
Detect Anomalies Using Amazon SageMaker (AIM420) - AWS re:Invent 2018Amazon Web Services
For a wide variety of metrics—including business metrics, application metrics, and low-level software and hardware metrics—it is critical to detect abnormalities to ensure that you end up with the right data. In this chalk talk, learn about the Random Cut Forest algorithm built into Amazon SageMaker in order to detect anomalies. We dive deep into detecting anomalies and tuning data in order to find practical solutions.
Debugging Gluon and Apache MXNet (AIM423) - AWS re:Invent 2018Amazon Web Services
In this chalk talk, we discuss how to troubleshoot the Gluon API for Apache MXNet from a PyCharm development environment by connecting to a remote server. We also discuss how to visualize the model and performance data using MXBoard.
Train Models on Amazon SageMaker Using Data Not from Amazon S3 (AIM419) - AWS...Amazon Web Services
Questions often arise about training machine learning models using Amazon SageMaker with data from sources other than Amazon S3. In this chalk talk, we dive deep into training models in real time using data from Amazon DynamoDB or a relational database. We demonstrate how training models with Amazon SageMaker is quick and easy, regardless of the data source.
[NEW LAUNCH!] [REPEAT 1] AWS DeepRacer Workshops –a new, fun way to learn rei...Amazon Web Services
Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on!
Go Global with Cloud-Native Architecture: Deploy AdTech Services Across Four ...Amazon Web Services
Plista, a Germany-based advertising solution provider, discusses how they use a cloud-native architecture, container-first approach to speed up development, increase agility, reduce latency, localize storage, and foster innovation and ownership in their organization. They demonstrate how a cloud blueprint is used to easily roll out their services to new global markets. With this architecture, Plista processes 1.7 billion requests per day, across four continents. They also discuss how they're adapting for GDPR compliance and redesigning parts of their platform to leverage new AWS services.
Detecting Financial Market Manipulation Using Machine Learning (AIM347) - AWS...Amazon Web Services
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.
Build an Intelligent Multi-Modal User Agent with Voice and NLU (AIM340) - AWS...Amazon Web Services
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.
Building, Training, and Deploying fast.ai Models Using Amazon SageMaker (AIM4...Amazon Web Services
In a short space of time, fast.ai has become a popular Deep Learning library, driven by the success of the fast.ai online Massive Open Online Course (MOOC). It has allowed SW developers to achieve, in the span of a few weeks, state-of-the-art results in domains such as Computer Vision (CV), Natural Language Processing (NLP), and structured data machine learning. In this chalk talk, we go into the details of building, training, and deploying fast.ai-based models using Amazon SageMaker.
Build Deep Learning Applications Using Apache MXNet, Featuring Workday (AIM40...Amazon Web Services
The Apache MXNet deep learning framework is used for developing, training, and deploying diverse AI applications, including computer vision, speech recognition, and natural language processing at scale. In this session, learn how to get started with MXNet on the Amazon SageMaker machine learning platform. Hear from Workday about how they built computer vision and natural language processing (NLP) models using MXNet to automatically extract information from paper documents, such as expense receipts and populate data records. Workday also shares its experience using Sockeye, an MXNet toolkit for quickly prototyping sequence-to-sequence NLP models.
Machine Learning Your Eight-Year-Old Would Be Proud Of (AIM390) - AWS re:Inve...Amazon Web Services
Come see examples of how Bebo uses Amazon SageMaker to power massive Fortnite tournaments every week. Traditional sports require referees, scorekeepers, field staff, and broadcast crews for every match. But esports are digital by nature. In this session, learn how machine learning and computer vision are enabling esports to occur at a massive scale. Learn how Bebo developed a model that can detect every victory and elimination, and can even prevent cheating on their tournament platform.
Alexa Everywhere: A Year in Review (ALX201) - AWS re:Invent 2018Amazon Web Services
Since its launch in 2015, Alexa has enabled new experiences across many device form factors at home, work, in the car, and on the go. With over 50,000 published skills, hundreds of new API features releases, and numerous Alexa-enabled devices, it can be hard to keep track with of the current pace. In this session, we get you up to speed on the current Voice First movement, the current Conversational AI trends, and we give demonstrations of some of the latest Alexa features and devices. Come learn about the new Alexa Skills Kit (ASK) multi-modal framework, Alexa Presentation Language (APL) for developers, Alexa skill fulfillment and consumables for customers, and some of the latest device offerings utilizing the Alexa Voice Service (AVS) and the new Alexa Gadgets Toolkit.
Unsupervised Learning with Amazon SageMaker (AIM333) - AWS re:Invent 2018Amazon Web Services
How do you use machine learning with data that isn't labeled? The unsupervised learning capabilities of Amazon SageMaker can easily handle unlabeled 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.
Build a Babel Fish with Machine Learning Language Services (AIM313) - AWS re:...Amazon Web Services
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 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).
Smarter Event-Driven Edge with Amazon SageMaker & Project Flogo (AIM204-S) - ...Amazon Web Services
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.
Modernizing Media Supply Chains with AWS Serverless (API301) - AWS re:Invent ...Amazon Web Services
Learn how Fox and Discovery modernized their media processing workflows to positively impact operations and business results. In this session, we examine each company's production architecture and learn how they utilize AWS services such as AWS Elemental Media Services, AWS Lambda, AWS Step Functions, Amazon API Gateway, and container toolsets. You also get insights into new business capabilities enabled by their AWS serverless architecture, including automation of content assembly and quality control as well as increased customer engagement with personalization and improved processing performance.
Tape Is a Four Letter Word: Back Up to the Cloud in Under an Hour (STG201) - ...Amazon Web Services
Tape backups. Yes, they're still a thing. If you want to stop using tapes but need to store immutable backups for compliance or operational reasons, attend this session to learn how to make an easy switch to a cloud-based virtual tape library (VTL). AWS Storage Gateway provides a seamless drop-in replacement for tape backups with its Tape Gateway. It works with the major backup software products, so you simply change the target for your backups, and they go to a VTL that stores virtual tapes on Amazon S3 and Amazon Glacier. Come see how it works.
Deep Dive on Amazon Rekognition, ft. Tinder & News UK (AIM307-R) - AWS re:Inv...Amazon Web Services
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.
M&E Leadership Session: The State of the Industry, What's New from AWS for M&...Amazon Web Services
In this wide-ranging keynote session, first hear from AWS VP Carla Stratfold on the major forces affecting the industry, then learn from AWS Global M&E Tech Lead Usman Shakeel about the latest and most exciting releases coming out of re:Invent relevant to the M&E industry. And finally, hear how technical leaders at the forefront of the industry are responding to accelerating changes in the media landscape.
Machine Learning at the Edge (AIM302) - AWS re:Invent 2018Amazon Web Services
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.
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.
Build Deep Learning Applications Using PyTorch and Amazon SageMaker (AIM432-R...Amazon Web Services
In this workshop, learn how to get started with the PyTorch deep learning framework using Amazon SageMaker, a fully managed platform to build, train, and deploy machine learning (ML) models at scale quickly and easily. First, we create a computer vision model using deep neural networks that helps us discover analytical information from our image dataset. Then, we use Amazon Redshift, a fully managed data warehouse, to perform analytics and find business value using the output of our ML model.
Provide Faster, Scalable Solutions to Support Research Use Cases with AWS (WP...Amazon Web Services
The Urban Institute chose a cloud-first strategy with AWS to increase the capacity and performance of big data and high-performance computing workloads for 300+ researchers. In this chalk talk, we demonstrate how our team piloted the execution of one of our signature microsimulation models in parallel, designing a new architecture in the cloud that required minimal changes to the original model’s source code. We also show how our team worked directly with AWS to develop open source code that launches powerful Amazon Elastic MapReduce (Amazon EMR) Spark clusters with minimal setup time, and combined it with our Elastic Cloud Computing Environment to allow for a simple Spark user experience.
Detect Anomalies Using Amazon SageMaker (AIM420) - AWS re:Invent 2018Amazon Web Services
For a wide variety of metrics—including business metrics, application metrics, and low-level software and hardware metrics—it is critical to detect abnormalities to ensure that you end up with the right data. In this chalk talk, learn about the Random Cut Forest algorithm built into Amazon SageMaker in order to detect anomalies. We dive deep into detecting anomalies and tuning data in order to find practical solutions.
Debugging Gluon and Apache MXNet (AIM423) - AWS re:Invent 2018Amazon Web Services
In this chalk talk, we discuss how to troubleshoot the Gluon API for Apache MXNet from a PyCharm development environment by connecting to a remote server. We also discuss how to visualize the model and performance data using MXBoard.
Train Models on Amazon SageMaker Using Data Not from Amazon S3 (AIM419) - AWS...Amazon Web Services
Questions often arise about training machine learning models using Amazon SageMaker with data from sources other than Amazon S3. In this chalk talk, we dive deep into training models in real time using data from Amazon DynamoDB or a relational database. We demonstrate how training models with Amazon SageMaker is quick and easy, regardless of the data source.
[NEW LAUNCH!] [REPEAT 1] AWS DeepRacer Workshops –a new, fun way to learn rei...Amazon Web Services
Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on!
Go Global with Cloud-Native Architecture: Deploy AdTech Services Across Four ...Amazon Web Services
Plista, a Germany-based advertising solution provider, discusses how they use a cloud-native architecture, container-first approach to speed up development, increase agility, reduce latency, localize storage, and foster innovation and ownership in their organization. They demonstrate how a cloud blueprint is used to easily roll out their services to new global markets. With this architecture, Plista processes 1.7 billion requests per day, across four continents. They also discuss how they're adapting for GDPR compliance and redesigning parts of their platform to leverage new AWS services.
Detecting Financial Market Manipulation Using Machine Learning (AIM347) - AWS...Amazon Web Services
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.
Build an Intelligent Multi-Modal User Agent with Voice and NLU (AIM340) - AWS...Amazon Web Services
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.
Building, Training, and Deploying fast.ai Models Using Amazon SageMaker (AIM4...Amazon Web Services
In a short space of time, fast.ai has become a popular Deep Learning library, driven by the success of the fast.ai online Massive Open Online Course (MOOC). It has allowed SW developers to achieve, in the span of a few weeks, state-of-the-art results in domains such as Computer Vision (CV), Natural Language Processing (NLP), and structured data machine learning. In this chalk talk, we go into the details of building, training, and deploying fast.ai-based models using Amazon SageMaker.
Build Deep Learning Applications Using Apache MXNet, Featuring Workday (AIM40...Amazon Web Services
The Apache MXNet deep learning framework is used for developing, training, and deploying diverse AI applications, including computer vision, speech recognition, and natural language processing at scale. In this session, learn how to get started with MXNet on the Amazon SageMaker machine learning platform. Hear from Workday about how they built computer vision and natural language processing (NLP) models using MXNet to automatically extract information from paper documents, such as expense receipts and populate data records. Workday also shares its experience using Sockeye, an MXNet toolkit for quickly prototyping sequence-to-sequence NLP models.
Machine Learning Your Eight-Year-Old Would Be Proud Of (AIM390) - AWS re:Inve...Amazon Web Services
Come see examples of how Bebo uses Amazon SageMaker to power massive Fortnite tournaments every week. Traditional sports require referees, scorekeepers, field staff, and broadcast crews for every match. But esports are digital by nature. In this session, learn how machine learning and computer vision are enabling esports to occur at a massive scale. Learn how Bebo developed a model that can detect every victory and elimination, and can even prevent cheating on their tournament platform.
Alexa Everywhere: A Year in Review (ALX201) - AWS re:Invent 2018Amazon Web Services
Since its launch in 2015, Alexa has enabled new experiences across many device form factors at home, work, in the car, and on the go. With over 50,000 published skills, hundreds of new API features releases, and numerous Alexa-enabled devices, it can be hard to keep track with of the current pace. In this session, we get you up to speed on the current Voice First movement, the current Conversational AI trends, and we give demonstrations of some of the latest Alexa features and devices. Come learn about the new Alexa Skills Kit (ASK) multi-modal framework, Alexa Presentation Language (APL) for developers, Alexa skill fulfillment and consumables for customers, and some of the latest device offerings utilizing the Alexa Voice Service (AVS) and the new Alexa Gadgets Toolkit.
Unsupervised Learning with Amazon SageMaker (AIM333) - AWS re:Invent 2018Amazon Web Services
How do you use machine learning with data that isn't labeled? The unsupervised learning capabilities of Amazon SageMaker can easily handle unlabeled 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.
Build a Babel Fish with Machine Learning Language Services (AIM313) - AWS re:...Amazon Web Services
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 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).
Smarter Event-Driven Edge with Amazon SageMaker & Project Flogo (AIM204-S) - ...Amazon Web Services
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.
Modernizing Media Supply Chains with AWS Serverless (API301) - AWS re:Invent ...Amazon Web Services
Learn how Fox and Discovery modernized their media processing workflows to positively impact operations and business results. In this session, we examine each company's production architecture and learn how they utilize AWS services such as AWS Elemental Media Services, AWS Lambda, AWS Step Functions, Amazon API Gateway, and container toolsets. You also get insights into new business capabilities enabled by their AWS serverless architecture, including automation of content assembly and quality control as well as increased customer engagement with personalization and improved processing performance.
Tape Is a Four Letter Word: Back Up to the Cloud in Under an Hour (STG201) - ...Amazon Web Services
Tape backups. Yes, they're still a thing. If you want to stop using tapes but need to store immutable backups for compliance or operational reasons, attend this session to learn how to make an easy switch to a cloud-based virtual tape library (VTL). AWS Storage Gateway provides a seamless drop-in replacement for tape backups with its Tape Gateway. It works with the major backup software products, so you simply change the target for your backups, and they go to a VTL that stores virtual tapes on Amazon S3 and Amazon Glacier. Come see how it works.
Lessons Learned from a Large-Scale Legacy Migration with Sysco (STG311) - AWS...Amazon Web Services
Migrating enterprise applications to the cloud requires thorough planning and consideration for a number of variables. Should you move your application to a similar infrastructure in the cloud (in a lift-and-shift scenario)? Or should you refactor your application to take advantage of cloud-native services for object storage, serverless, auto-scaling, and so on? In this session, an AWS expert walks through the ten commandments that enterprises should follow when moving applications to the cloud and refactoring them for optimal performance. Then, a representative of Sysco Corporation, a Fortune 50 company, shares how the company migrated mission-critical legacy business systems and modernized them to take advantage of the AWS Cloud. Learn how the company moved its enterprise purchasing system, which processes millions of dollars in sales daily, to the AWS Cloud while achieving a 60% decrease in run costs. Also discover the lessons learned and highlights of the migration, which resulted in 30% increase in performance, 3x improvement in user accessibility, and a significant decrease in order backlogs and outages.
Amazon Prime Video: Delivering the Amazing Video Experience (CTD203-R1) - AWS...Amazon Web Services
In this session, hear engineers from Amazon Prime Video and Amazon CloudFront discuss how they have architected and optimized their video delivery for scaled global audiences. Topics include optimizing the application and video pipeline for use with content delivery networks (CDN), optimizations in the CDN for efficient and performant video delivery, measuring quality, and effectively managing multi-CDN performance and policy. Learn how CloudFront delivers the performance that Prime Video demands, and hear best practices and lessons learned through scaling this fast-growing service.
Chris Bond (Founder, AWS Thinkbox)
Jason Fotter (CTO and Co-Founder, FuseFX)
More production work is being done in the cloud than ever before, yet we have only begun to scratch the surface of the performance and scale that the cloud can bring to many facets of production. Join Chris Bond, Founder of Thinkbox Software and Director Product at AWS, to learn how some of our customers are making the transition and building stable pipelines to move their assets and shots to the cloud. He will also be joined by Jason Fotter, Co-Founder and CTO of FuseFX, who will focus on how FuseFX has taken advantage of the cloud with AWS and how they have tackled key rendering and storage problems.
Microservices, containers, serverless - these industry buzzwords are hot right now. Breaking down monolithic applications and architectures is a central theme across industries as organizations move to adopt new technologies and take advantage of the AWS cloud to scale, while rapidly innovating to meet changing customer expectations and competitive challenges. In this session, we'll take a closer look at what is actually required to "break down the monolith" and provide some strategies and design patterns for building microservices on AWS.
Cloud-Based Media Archival, Media Asset Management, & Supply Chain Workflows ...Amazon Web Services
Media and entertainment companies are leveraging the on-demand, pay-as-you-go benefits of AWS to cost-effectively create movies, stream videos, broadcast programs, publish content around the world, and store and preserve their media at scale. In this chalk talk, we diagram common reference architectures deployed by some of the top media and entertainment companies that use Amazon S3 and Amazon Glacier for active and long-term media archives, and we share best practices for media asset management (MAM), supply chain, and content distribution. We also discuss ways to optimize TCO.
How One Growing U.S. County Protects Residents' Data on AWSAmazon Web Services
Join our webinar to hear how NetApp® AltaVault™ is helping King County and other large, data-driven organizations such as the University of Wollongong transition from tape to cloud backup with Amazon Web Services. AltaVault, available in AWS Marketplace and through NetApp and their Channel Partners, helps customers back up and recover their data easily and quickly.
How Element 84 Raises the Bar on Streaming Satellite DataAmazon Web Services
GOES-16 is a source of critical data for monitoring smoke, flooding impacts, burn scars, volcanic ash, and weather. However, finding and using this data can require significant investment. Element 84 married video compression and streaming technology with NASA’s Cumulus data processing pipeline, plus AWS Managed Services, to make the entire GOES-16 archive interactive on an array of formats. Users can now easily identify dates of interest for events like natural disasters, and stage a subset of the archive for analysis. And all of this scales down to $0 when not in use.
What’s New for Amazon DynamoDB - 2018 Q1 Update - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Learn how adaptive capacity and Time-To-Live (TTL) can dynamically scale your tables
- Learn how to design your DynamoDB for global applications with Global Tables
- Learn how to perform On-Demand Backup on your DynamoDB tables for data archival
How Fannie Mae Processes over a Quarter Million Loans per Day with Amazon S3 ...Amazon Web Services
In this session, Fannie Mae discusses how they completely re-architected a mission-critical application using AWS native services that process hundreds of thousands of mortgage loans every day in a highly scalable and reliable manner. The transaction-heavy workload uses over 20+ million Amazon S3 transactions a day, each within 150-millisecond response times, thus providing increased uptime and faster response.
CMP376 - Another Week, Another Million Containers on Amazon EC2aspyker
Netflix’s container management platform, Titus, powers critical aspects of the Netflix business, including video streaming, recommendations, machine learning, big data, content encoding, studio technology, internal engineering tools, and other Netflix workloads. Titus offers a convenient model for managing compute resources, enables developers to maintain just their application artifacts, and provides a consistent developer experience from a developer’s laptop to production by leveraging Netflix container-focused engineering tools.
Another Week, Another Million Containers on Amazon EC2 (CMP376) - AWS re:Inve...Amazon Web Services
Netflix’s container management platform, Titus, powers critical aspects of the Netflix business, including video streaming, recommendations, machine learning, big data, content encoding, studio technology, internal engineering tools, and other Netflix workloads. Titus offers a convenient model for managing compute resources, enables developers to maintain just their application artifacts, and provides a consistent developer experience from a developer’s laptop to production by leveraging Netflix container-focused engineering tools.
Come scalare da zero ai tuoi primi 10 milioni di utenti.pdfAmazon Web Services
AWS Summit Milano 2018
Come scalare da zero ai tuoi primi 10 milioni di utenti
Speaker: Giorgio Bonfiglio, AWS Technical Account Manager - Enterprise Support
How Different Large Organizations are Approaching Cloud AdoptionAmazon Web Services
The implementation of highly scalable, easy-to-deploy technology is transforming enterprises, but it’s not a one-size-fits-all approach. Organizations begin their cloud adoption journeys in many ways. Some start with pilot projects and others jump into mission-critical programs, but they are all starting with an existing infrastructure. Adopting cloud doesn’t mean scrapping it all and starting over. This session explores how organizations are using cloud while building on their existing technology and lessons they’ve learned along the way. In this session we will discuss when and how to leverage hybrid cloud computing to meet the needs of the enterprise. We will cover popular hybrid cloud use cases in enterprises, pillars to design a secure hybrid cloud environment and how to get started with AWS.
Building a Hybrid Architecture: Enterprise Backup & Recovery (ENT212-S) - AWS...Amazon Web Services
Have you ever had sleepless nights because you couldn't meet your Recovery Point and Time Objectives? What about recovering data in the event of a disaster? If you're a backup or storage architect, the answer is most likely "yes." Come to this session to learn how Cohesity can help you build an enterprise-grade solution for long-term retention, development and testing, and disaster recovery. Hear how Airbud Entertainment is using the Cohesity DataPlatform and AWS storage services, such as Amazon S3, and Amazon Glacier, to simplify their backup and long-term retention strategy and architecture. This session is brought to you by AWS partner, Cohesity, Inc.
Scaling Up to Your First 10 Million Users (ARC205-R1) - AWS re:Invent 2018Amazon Web Services
Cloud computing provides a number of advantages, such as the ability to scale your web application or website on demand. If you have a new web application and want to use cloud computing, you might be asking yourself, "Where do I start?" Join us in this session for best practices on scaling your resources from one to millions of users. We show you how to best combine different AWS services, how to make smarter decisions for architecting your application, and how to scale your infrastructure in the cloud.
by Henry Zhang, Sr. Product Manager, AWS
Compared to storing long-term datasets on-premise, archiving in the cloud is a smart alternative whether you’re looking for an active archive solution, tape replacement, or to fulfill a compliance requirement. Learn how AWS customers are simplifying their archiving strategies and meeting compliance needs using Amazon Glacier.
Cloud computing gives you a number of advantages, such as the ability to scale your web application or website on demand. If you have a new web application and want to use cloud computing, you might be asking yourself, "Where do I start?" Join us to understand best practices for scaling your resources from one to millions of users. We’ll show you how to best combine different AWS services, how to make smarter decisions for architecting your application, and how to scale your infrastructure in the cloud.
Similar to Hollywood's Cloud-Based Content Lakes: Modernized Media Archives (MAE203) - AWS re:Invent 2018 (20)
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.