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.
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.
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.
Hollywood's Cloud-Based Content Lakes: Modernized Media Archives (MAE203) - A...Amazon Web Services
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.
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.
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.
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.
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.
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.
Hollywood's Cloud-Based Content Lakes: Modernized Media Archives (MAE203) - A...Amazon Web Services
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.
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.
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.
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.
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.
Financial Svcs: Mine Actionable Insights from Customer Interactions Using Mac...Amazon Web Services
In this hands-on workshop, learn how financial services companies can apply AWS streaming, storage, and analytics services to massive volumes of customer data generated by multichannel communications. These services can combine to form a near real-time alert system that flags and responds to issues quickly. We show you how to create a serverless framework using AWS Lambda, Amazon Kinesis, Amazon S3, and AWS KMS to securely capture and process customer interactions through voice and chat. This architecture supports the level of encryption and privacy required by financial services while demonstrating how your organization can use AI/ML services like Amazon Comprehend, Amazon Transcribe, and Amazon Translate to score every single customer interaction to determine satisfaction and improve customer retention statistics. Expect to leave this workshop understanding how to use AWS to track, analyze, and respond to service interactions and complaint data with financial services security standards in place.
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.
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).
Build Modern Applications that Align with Twelve-Factor Methods (API303) - AW...Amazon Web Services
Twelve-Factor designs improve component reuse and resilience for developers building large-scale software-as-a-service (SaaS) applications. In recent years, the Twelve-Factor guidelines have become a source of best practices for both developers and operations engineers, regardless of the application’s use case and at nearly any scale. In this workshop, create a modern app to see how the Twelve-Factor Application guidelines align with serverless best practices. Learn how to address those Twelve-Factor guidelines that don’t directly align with serverless architectures or are interpreted differently, and practice by implementing examples using AWS Lambda, AWS Step Functions, Amazon API Gateway, and the AWS Code services. Bring a laptop (Windows/OSX/Linux all supported). Tablets are not appropriate. We also recommend installing the current version of Chrome or Firefox.
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.
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.
Build, Train, and Deploy ML Models with Amazon SageMaker (AIM410-R2) - AWS re...Amazon Web Services
Come and help build the most accurate text classification model possible. A fully managed machine learning (ML) platform, Amazon SageMaker enables developers and data scientists to build, train, and deploy ML models using built-in or custom algorithms. In this workshop, you learn how to leverage Keras/TensorFlow deep learning frameworks to build a text classification solution using custom algorithms on Amazon SageMaker. You package custom training code in a Docker container, test it locally, and then use Amazon SageMaker to train a deep learning model. You then try to iteratively improve the model to achieve a higher level of accuracy. Finally, you deploy the model in production so different applications within the company can start leveraging this ML classification service. Please note that to actively participate in this workshop, you need an active AWS account with admin-level IAM permissions to Amazon SageMaker, Amazon Elastic Container Registry (Amazon ECR), and Amazon S3.
Tailor Your Alexa Skill Responses to Deliver Truly Personal Experiences (ALX3...Amazon Web Services
Delivering truly personal responses to customers is one of the most engaging features of an Alexa skill. In this session, learn the different approaches and best practices in creating responses that are tailored to each one of your customers. By applying what you learn, you can keep them coming back to your voice experience.
Accelerate ML Training on Amazon SageMaker Using GPU-Based EC2 P3 Instances (...Amazon Web Services
In this workshop, you gain hands-on experience in training deep learning neural networks with Amazon SageMaker using GPU-based EC2 P3 instances. Amazon EC2 P3 instances, powered by NVIDIA Tesla V100 GPUs, offer the highest-performing GPU-based instances in the cloud for efficient model training. We discuss and work through building convolution neural networks for solving common computer vision problems. All attendees must bring their own laptop (Windows, macOS, and Linux all supported). Tablets are not appropriate. We also recommend having the current version of Chrome or Firefox installed.
Serverless Architectural Patterns and Best Practices (ARC305-R2) - AWS re:Inv...Amazon Web Services
As serverless architectures become more popular, customers need a framework of patterns to help them identify how to leverage AWS to deploy their workloads without managing servers or operating systems. This session describes reusable serverless patterns while considering costs. For each pattern, we provide operational and security best practices and discuss potential pitfalls and nuances. We also discuss the considerations for moving an existing server-based workload to a serverless architecture. This session can help you recognize candidates for serverless architectures in your own organizations and understand areas of potential savings and increased agility.
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.
Alexa for Device Makers: Create Products with Alexa Built-In Using AVS (ALX30...Amazon Web Services
In this hands-on workshop, learn how to use Alexa Built-In to create products that you can talk to and use to access music, information, control smart-home devices, and all of Alexa's skills. We use the C++-based AVS Device SDK and a Raspberry Pi to access the cloud-based Alexa Voice Service (AVS). Leave this session with your own working prototype and the knowledge to bring your products to market.
In this session, learn from market-leader Vonage how and why they re-architected their QoS-sensitive, highly available and highly performant legacy real-time communications systems to take advantage of Amazon EC2, Enhanced Networking, Amazon S3, ASG, Amazon RDS, Amazon ElastiCache, AWS Lambda, StepFunctions, Amazon SNS, Amazon SQS, Amazon Kinesis, Amazon EFS, and more. We also learn how Aspect, a multinational leader in call center solutions, used AWS Lambda, Amazon API Gateway, Amazon Kinesis, Amazon ElastiCache, Amazon Cognito, and Application Load Balancer with open-source API development tooling from Swagger, to build a comprehensive, microservices-based solution. Vonage and Aspect share their journey to TCO optimization, global outreach, and agility with best practices and insights.
Deep Dive on Amazon S3 Storage Classes: Creating Cost Efficiencies across You...Amazon Web Services
"Amazon S3 supports a range of storage classes that can help you cost-effectively store data without impacting performance or availability. Each of our storage classes offer different data-access levels, retrieval times, and costs to support various use cases. In this session, Amazon S3 experts dive deep into the different Amazon S3 storage classes, their respective attributes, and when you should use them.
"
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.
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.
Market Prediction Using ML: Experiment with Amazon SageMaker and the Deutsche...Amazon Web Services
In this workshop, learn how to use machine learning to analyze the Deutsche Börse Public Dataset, which consists of trade data aggregated to one-minute intervals from the Tradex and Eurex engines, comprising a variety of equities, funds, and derivative securities. The public dataset provides initial price, lowest price, highest price, final price, and volume for every minute of the trading day, and for every tradeable security. Learn how to apply a variety of ML models to the data to find patterns and methods to predict price movements or identify trends in the market. Also learn how to interact with data staged for analysis in Amazon S3, use AWS Glue to transform data into analysis-ready formats, and use Amazon SageMaker and Amazon EC2 to experiment with a variety of ML models to derive insights from the data.
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 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.
Voice of the Customer Analytics with Natural Language Processing - AWS Summit...Amazon Web Services
Voice of the Customer Analytics with Natural Language Processing
This session explores the use of building a comprehensive natural language processing pipeline to perform text analytics to better understand how to respond to and improve customer experience. Attendees will learn how to use AWS services including Amazon Comprehend and Amazon Transcribe to process and perform sentiment analysis on voice of the customer data such as customer call recordings and social media.
Tom McMeekin, Solutions Architect, Amazon Web Services
Build a Voice-based Chatbot for Your Amazon Connect Contact Center - SRV326 -...Amazon Web Services
Learn how easy it is to incorporate a voice-based Amazon Lex chatbot into your Amazon Connect contact center. In this workshop, we walk you through configuring your own Amazon Connect contact center, implementing a chatbot, and using it in your workflows to deliver a personalized voice-based caller experience. You also learn to further personalize caller experiences by using AWS Lambda to access caller information from your customer data system. Leave the workshop with a functioning Amazon Connect contact center and a voice-enabled chatbot that you can continually modify to your business needs over time. Come prepared to build by bringing your laptop and a phone to make test calls
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.
Financial Svcs: Mine Actionable Insights from Customer Interactions Using Mac...Amazon Web Services
In this hands-on workshop, learn how financial services companies can apply AWS streaming, storage, and analytics services to massive volumes of customer data generated by multichannel communications. These services can combine to form a near real-time alert system that flags and responds to issues quickly. We show you how to create a serverless framework using AWS Lambda, Amazon Kinesis, Amazon S3, and AWS KMS to securely capture and process customer interactions through voice and chat. This architecture supports the level of encryption and privacy required by financial services while demonstrating how your organization can use AI/ML services like Amazon Comprehend, Amazon Transcribe, and Amazon Translate to score every single customer interaction to determine satisfaction and improve customer retention statistics. Expect to leave this workshop understanding how to use AWS to track, analyze, and respond to service interactions and complaint data with financial services security standards in place.
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.
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).
Build Modern Applications that Align with Twelve-Factor Methods (API303) - AW...Amazon Web Services
Twelve-Factor designs improve component reuse and resilience for developers building large-scale software-as-a-service (SaaS) applications. In recent years, the Twelve-Factor guidelines have become a source of best practices for both developers and operations engineers, regardless of the application’s use case and at nearly any scale. In this workshop, create a modern app to see how the Twelve-Factor Application guidelines align with serverless best practices. Learn how to address those Twelve-Factor guidelines that don’t directly align with serverless architectures or are interpreted differently, and practice by implementing examples using AWS Lambda, AWS Step Functions, Amazon API Gateway, and the AWS Code services. Bring a laptop (Windows/OSX/Linux all supported). Tablets are not appropriate. We also recommend installing the current version of Chrome or Firefox.
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.
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.
Build, Train, and Deploy ML Models with Amazon SageMaker (AIM410-R2) - AWS re...Amazon Web Services
Come and help build the most accurate text classification model possible. A fully managed machine learning (ML) platform, Amazon SageMaker enables developers and data scientists to build, train, and deploy ML models using built-in or custom algorithms. In this workshop, you learn how to leverage Keras/TensorFlow deep learning frameworks to build a text classification solution using custom algorithms on Amazon SageMaker. You package custom training code in a Docker container, test it locally, and then use Amazon SageMaker to train a deep learning model. You then try to iteratively improve the model to achieve a higher level of accuracy. Finally, you deploy the model in production so different applications within the company can start leveraging this ML classification service. Please note that to actively participate in this workshop, you need an active AWS account with admin-level IAM permissions to Amazon SageMaker, Amazon Elastic Container Registry (Amazon ECR), and Amazon S3.
Tailor Your Alexa Skill Responses to Deliver Truly Personal Experiences (ALX3...Amazon Web Services
Delivering truly personal responses to customers is one of the most engaging features of an Alexa skill. In this session, learn the different approaches and best practices in creating responses that are tailored to each one of your customers. By applying what you learn, you can keep them coming back to your voice experience.
Accelerate ML Training on Amazon SageMaker Using GPU-Based EC2 P3 Instances (...Amazon Web Services
In this workshop, you gain hands-on experience in training deep learning neural networks with Amazon SageMaker using GPU-based EC2 P3 instances. Amazon EC2 P3 instances, powered by NVIDIA Tesla V100 GPUs, offer the highest-performing GPU-based instances in the cloud for efficient model training. We discuss and work through building convolution neural networks for solving common computer vision problems. All attendees must bring their own laptop (Windows, macOS, and Linux all supported). Tablets are not appropriate. We also recommend having the current version of Chrome or Firefox installed.
Serverless Architectural Patterns and Best Practices (ARC305-R2) - AWS re:Inv...Amazon Web Services
As serverless architectures become more popular, customers need a framework of patterns to help them identify how to leverage AWS to deploy their workloads without managing servers or operating systems. This session describes reusable serverless patterns while considering costs. For each pattern, we provide operational and security best practices and discuss potential pitfalls and nuances. We also discuss the considerations for moving an existing server-based workload to a serverless architecture. This session can help you recognize candidates for serverless architectures in your own organizations and understand areas of potential savings and increased agility.
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.
Alexa for Device Makers: Create Products with Alexa Built-In Using AVS (ALX30...Amazon Web Services
In this hands-on workshop, learn how to use Alexa Built-In to create products that you can talk to and use to access music, information, control smart-home devices, and all of Alexa's skills. We use the C++-based AVS Device SDK and a Raspberry Pi to access the cloud-based Alexa Voice Service (AVS). Leave this session with your own working prototype and the knowledge to bring your products to market.
In this session, learn from market-leader Vonage how and why they re-architected their QoS-sensitive, highly available and highly performant legacy real-time communications systems to take advantage of Amazon EC2, Enhanced Networking, Amazon S3, ASG, Amazon RDS, Amazon ElastiCache, AWS Lambda, StepFunctions, Amazon SNS, Amazon SQS, Amazon Kinesis, Amazon EFS, and more. We also learn how Aspect, a multinational leader in call center solutions, used AWS Lambda, Amazon API Gateway, Amazon Kinesis, Amazon ElastiCache, Amazon Cognito, and Application Load Balancer with open-source API development tooling from Swagger, to build a comprehensive, microservices-based solution. Vonage and Aspect share their journey to TCO optimization, global outreach, and agility with best practices and insights.
Deep Dive on Amazon S3 Storage Classes: Creating Cost Efficiencies across You...Amazon Web Services
"Amazon S3 supports a range of storage classes that can help you cost-effectively store data without impacting performance or availability. Each of our storage classes offer different data-access levels, retrieval times, and costs to support various use cases. In this session, Amazon S3 experts dive deep into the different Amazon S3 storage classes, their respective attributes, and when you should use them.
"
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.
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.
Market Prediction Using ML: Experiment with Amazon SageMaker and the Deutsche...Amazon Web Services
In this workshop, learn how to use machine learning to analyze the Deutsche Börse Public Dataset, which consists of trade data aggregated to one-minute intervals from the Tradex and Eurex engines, comprising a variety of equities, funds, and derivative securities. The public dataset provides initial price, lowest price, highest price, final price, and volume for every minute of the trading day, and for every tradeable security. Learn how to apply a variety of ML models to the data to find patterns and methods to predict price movements or identify trends in the market. Also learn how to interact with data staged for analysis in Amazon S3, use AWS Glue to transform data into analysis-ready formats, and use Amazon SageMaker and Amazon EC2 to experiment with a variety of ML models to derive insights from the data.
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 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.
Voice of the Customer Analytics with Natural Language Processing - AWS Summit...Amazon Web Services
Voice of the Customer Analytics with Natural Language Processing
This session explores the use of building a comprehensive natural language processing pipeline to perform text analytics to better understand how to respond to and improve customer experience. Attendees will learn how to use AWS services including Amazon Comprehend and Amazon Transcribe to process and perform sentiment analysis on voice of the customer data such as customer call recordings and social media.
Tom McMeekin, Solutions Architect, Amazon Web Services
Build a Voice-based Chatbot for Your Amazon Connect Contact Center - SRV326 -...Amazon Web Services
Learn how easy it is to incorporate a voice-based Amazon Lex chatbot into your Amazon Connect contact center. In this workshop, we walk you through configuring your own Amazon Connect contact center, implementing a chatbot, and using it in your workflows to deliver a personalized voice-based caller experience. You also learn to further personalize caller experiences by using AWS Lambda to access caller information from your customer data system. Leave the workshop with a functioning Amazon Connect contact center and a voice-enabled chatbot that you can continually modify to your business needs over time. Come prepared to build by bringing your laptop and a phone to make test calls
Build Text Analytics Solutions with AWS ML Services: Machine Learning Worksho...Amazon Web Services
Machine Learning Workshops at the San Francisco Loft
Build Text Analytics Solutions with Amazon Comprehend and Amazon Translate
Natural language holds a wealth of information like user sentiment and conversational intent. In this session, we'll demonstrate the capabilities of Amazon Comprehend, a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. We'll show you how to build a VOC (Voice of the Customer) application and integrate it with other AWS services including AWS Lambda, Amazon S3, Amazon Athena, Amazon QuickSight, and Amazon Translate. We’ll also show you additional methods for NLP available through Amazon Sagemaker.
Level: 200-300
Speaker: Ben Snively - Principal Solutions Architect, Data & Analytics, AWS
Optimizing Healthcare Call Centers with Natural Language Understanding (HLC30...Amazon Web Services
Large call volumes into customer service call centers can lead to frustrated customers, delayed responses, and overburdened staff, particularly when a large number of queries could have been resolved with simple yes or no answers, or formulaic responses. In this session hear from the National Health Service (NHS) Business Services Authority, the support body for multiple NHS organizations in England, how it is using machine learning to manage the large volumes of calls coming to their call center (five million calls each year). Learn how these services have been used to reduce call response times, increase staff morale, and maximize staff utilization for value-add activities. See how to develop and implement Amazon Connect, Amazon Lex, and Amazon Polly to automate call centers, reduce labor costs, and provide a consistent experience for customers.
Mike Gillespie - Build Intelligent Applications with AWS ML Services (200).pdfAmazon Web Services
Organizations are increasingly turning to machine learning to build intelligent applications and get more insights out of their data in real-time. In this session, you’ll learn about AWS Machine Learning APIs for computer vision and language, and how to get started with these pre-trained services: Amazon Rekognition, Amazon Comprehend, Amazon Transcribe, Amazon Translate, Amazon Polly, and Amazon Lex. We’ll also show how these services connect to AWS’s comprehensive data platform and services to drive the success of your machine learning projects.
AWS Immersion Day - Image Data Insights & Analytics Specialist Session - June...Amazon Web Services
Learn how to incorporate video data and analytics into your data management and business decision process. Discover how industry leaders are using AWS to do the heavy lifting with image data and innovating quickly. Our specialists will cover common issues and provide best practices from using IoT devices to collect data to training a ML model to using these models on the edge without network connectivity.
Kate Werling - Build text analytics solutions with AWS ML Services (300) _BP.pdfAmazon Web Services
Natural language holds a wealth of information like user sentiment and conversational intent. In this session, we'll demonstrate the capabilities of Amazon Comprehend, a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. We'll show you how to build a VOC (Voice of the Customer) application and integrate it with other AWS services including AWS Lambda, Amazon S3, Amazon Athena, Amazon QuickSight, and Amazon Translate. We’ll also show you additional methods for NLP available through Amazon Sagemaker.
Build Text Analytics Solutions with Amazon Comprehend & Amazon TranslateAmazon Web Services
Natural language holds a wealth of information like user sentiment and conversational intent. In this session, we'll demonstrate the capabilities of Amazon Comprehend, a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. We'll show you how to build a VOC (Voice of the Customer) application and integrate it with other AWS services including AWS Lambda, Amazon S3, Amazon Athena, Amazon QuickSight, and Amazon Translate. We’ll also show you additional methods for NLP available through Amazon Sagemaker.
Automate for Efficiency with Amazon Transcribe and Amazon TranslateAmazon Web Services
Teaching a computer how to understand human language is one of the most challenging problems in computer science. However, significant progress has been made in automatic speech recognition (ASR) and machine translation (MT) to create highly accurate and fluent transcriptions and translations. Amazon Transcribe is an ASR service that makes it easy for developers to add speech to text capability to their applications, and Amazon Translate is a MT service that delivers fast, high-quality, and affordable language translation. In this session, you’ll learn how to weave machine translation and transcription into your workflows, to increase the efficiency and reach of your operations.
Build Text Analytics Solutions with Amazon Comprehend & Amazon Translate: Mac...Amazon Web Services
Machine Learning Week at the San Francisco Loft: Build text analytics solutions with Amazon Comprehend & Amazon Translate
Natural language holds a wealth of information like user sentiment and conversational intent. In this session, we'll demonstrate the capabilities of Amazon Comprehend, a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. We'll show you how to build a VOC (Voice of the Customer) application and integrate it with other AWS services including AWS Lambda, Amazon S3, Amazon Athena, Amazon QuickSight, and Amazon Translate. We’ll also show you additional methods for NLP available through Amazon Sagemaker.
Level: 200-300
Speaker: Anjana Kandalam - Solutions Architect, AWS
Natural language holds a wealth of information like user sentiment and conversational intent. In this session, we'll demonstrate the capabilities of Amazon Comprehend, a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. We'll show you how to build a VOC (Voice of the Customer) application and integrate it with other AWS services including AWS Lambda, Amazon S3, Amazon Athena, Amazon QuickSight, and Amazon Translate. We’ll also show you additional methods for NLP available through Amazon Sagemaker.
Level: 200-300
Speaker: Yash Pant - Enterprise Solutions Architect, AWS
Increase the Value of Video with Machine Learning & Media Services - SRV322 -...Amazon Web Services
In this session, learn how to generate metadata from media, and make videos searchable by objects, people, activities, dialogs, and more by using Amazon Machine Learning (Amazon ML) tools. Learn how to make videos more valuable and enable a wide range of use cases, including searching and indexing video libraries. Learn how to use AWS Elemental MediaConvert to create video highlight clips from keywords, automatically or on demand, for content like sports videos and from sources like nature cameras or security feeds. Finally, use Amazon Transcribe and Amazon Translate with AWS Media Services to produce captions and translations to expand audience reach for corporate videos, marketing and sales material, and training videos.
AI & ML at Amazon: AWS Developer Workshop - Web Summit 2018Amazon Web Services
AI & Machine Learning at Amazon: AWS Developer Workshop - Web Summit 2018
Amazon has been applying machine learning to create artifical intelligence features within its products and services for over 20 years. Join this session and learn about the application of ML and AI within Amazon, from retail product recommendations to the latest in natural language understanding, and how you can use easily accessible services from AWS to enable you to include AI features within your applications or build your own custom ML models for your own specific AI use cases.
Speaker: Ian Massingham - Director, Technical Evangelist, AWS
Increase the Value of Video with ML & Media Services - SRV322 - Toronto AWS S...Amazon Web Services
Learn how to generate metadata from your media and make videos searchable by objects, people, activities, dialog, and more by using Amazon Machine Learning tools. Learn how to make videos more valuable and enable a wide range of use cases, including searching and indexing your video library. Learn how to use AWS Elemental MediaConvert to create video highlight clips from keywords, automatically or on demand, for content like sports videos and from sources like nature cameras or security feeds. Finally, learn how to use Amazon Transcribe and Amazon Translate with AWS Media Services to produce captions and translations to expand audience reach for corporate videos, marketing and sales material, and training videos.
Increase the Value of Video with ML & Media Services - SRV322 - Anaheim AWS S...Amazon Web Services
In this session, learn how to generate metadata from your media and make your videos searchable by objects, people, activities, dialog, and more by using Amazon Machine Learning tools. Also learn how to make videos more valuable and enable a wide range of use cases, including searching and indexing your video library. We show you how to use AWS Elemental MediaConvert to create video highlight clips from keywords, automatically or on demand, for content like sports videos and from sources like nature cameras or security feeds. Finally, we cover how to use Amazon Transcribe and Amazon Translate with AWS Media Services to produce captions and translations to expand audience reach for corporate videos, marketing and sales material, and training videos.
Maximizing the Customer Experience with AI on AWS - MCL302 - re:Invent 2017Amazon Web Services
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.
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.
Similar to Capture Voice of Customer Insights with NLP & Analytics (AIM415-R1) - 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.