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ADM201-L - Leadership session: Digital marketing and ad technology In this session meant for executive leaders, you get to see the AWS vision for how companies stand out in the crowded, massively scaled advertising and marketing ecosystem. Industry leaders share stories about how they used AWS to enable breakthroughs in areas such as customer data collection, identity resolution, audience targeting, programmatic advertising, personalization, and measurement. Then we turn to the future and share our playbook for artificial intelligence and real-time solutions for transforming big data into marketing outcomes. Session Dmitri Tchikatilov
ADM203 - Reimagining advertising analytics & identity resolution at scale Most companies in Advertising & Marketing run some kind of big data workload. But are they taking advantage of the latest cloud technology? In this session, learn how AWS customers can optimize data collection, analytics, and identity resolution using containers, serverless computing, and graph databases. Customers share detailed technical best practices for big data and advertising analytics at massive scale and low cost. Then we dive deep into an example of how to improve identity matching and audience targeting using Amazon Neptune and other AWS tools. Session Dmitri Tchikatilov Christopher Hansen Sasikala Singamaneni
ADM301 - Best practices for identity resolution with Amazon Neptune In this chalk talk, learn how to build a cloud-centric, graph-based identity resolution system that connects customer data across devices, channels, and touchpoints and helps enable better media buying and personalization. Learn about best practices and the mistakes to avoid for identity resolution data collection, processing, and preparation. We deep dive into architectural details for achieving high availability and low latency at scale using AWS services such as Amazon EMR, Amazon Neptune, Amazon EC2, and Amazon S3. We also discuss recommendations for using Neptune as a fully managed graph database service for identity resolution. Chalk Talk Taylor Riggan Nitin Poddar
ADM302 - End-to-end machine learning using Spark and Amazon SageMaker Learn how AWS customers are developing production-ready machine learning models to optimize auction dynamics and bid pricing—all within the millisecond latency requirements of programmatic ad buying. Forget “hello world” ML tutorials; instead we dive deep into an example of how to train models for terabyte-scale advertising data cost-effectively. Find out how to create environments for machine learning engineers so they can prototype and explore with TensorFlow before executing it in distributed systems using Spark and Amazon SageMaker. We explain productionizing models and deployment, which we then back up with real-life examples. Session Dmitri Tchikatilov Janko Kovačević
ADM303 - Implementing Amazon Personalize across marketing channels Amazon Personalize makes it easy to train machine learning models based on the behavioral data of your customers. However, putting these models into practice across marketing channels to deliver high-quality customer experiences still requires some lifting on your end. In this chalk talk, we discuss the best practices and strategies for implementing Amazon Personalize—including what we’ve seen work and what we’ve seen doesn’t—to create personalized customer experiences across multiple digital channels, including web, mobile, and messaging. Chalk Talk James Jory
ADM401 - Analyzing Amazon DSP data in an AWS data lake Advertisers and agencies working with Amazon DSP are interested in analyzing available data points to understand segment and campaign performance. In this one-hour lab meant for companies working with Amazon DSP today, we show how to set up an AWS data lake based on reporting files from Amazon DSP in Amazon Simple Storage Service (Amazon S3) in order to analyze and visualize the data. We show you how to use AWS Glue Data Catalog to crawl and create a data source, use Amazon Athena for extraction, and display the data in Amazon QuickSight. Builders Session David Beck
ADM402 - Testing techniques for measuring personalization In this hands-on lab, you sit down with an AWS solutions architect to learn how to integrate techniques such as A/B, multi-armed bandit, and interleaved recommendation testing to measure effectiveness of machine learning for personalization. You’ll come away knowing how to build an end-to-end solution for deploying recommendation models using Amazon Personalize that integrates with measurement techniques that can be applied to customer experience in e-commerce or other marketing channels. Builders Session James Jory
AIM201-S - Hot paths to anomaly detection with TIBCO data science, streaming on AWS Sensor data on the event stream can be voluminous. In NAND manufacturing, there are millions of columns of data that represent many measured and virtual metrics. These sensor data can arrive with considerable velocity. In this session, learn about developing cross-sectional and longitudinal analyses for anomaly detection and yield optimization using deep learning methods, as well as super-fast subsequence signature search on accumulated time-series data and methods for handling very wide data in Apache Spark on Amazon EMR. The trained models are developed in TIBCO Data Science and Amazon SageMaker and applied to event streams using services such as Amazon Kinesis to identify hot paths to anomaly detection. This presentation is brought to you by TIBCO Software, an APN Partner. Session Steven Hillion Michael OConnell
AIM202-S - PwC’s SENTRI solution for HC/LS case management SENTRI is an intelligent automation application platform built leveraging native AWS components to facilitate case processing in the Healthcare industry. PwC built an engine that can take in an adverse healthcare/level of service (HC/LS) event case, extract key information, provide an initial interpretation of severity, and triage the case for review. PwC performed analysis using a user-centric experience, which allowed the case processor to easily verify outputs and helped build trust and confidence in the machine’s interpretation. In this session, learn how a PwC customer has been successfully using this system for over nine months. It used to take two hours to process a case. Now, it takes three seconds. This presentation is brought to you by PwC, an APN Partner. Session Matthew Rich
AIM203-S - Take AI/ML from theory to practice with Intel technologies on AWS The challenges associated with scalability have been removed in the cloud. Today, organizations deploy tons of artificial intelligence/machine learning (AI/ML) workloads on AWS. Learn about how easy and cost-effective it is to build customized, intelligent data models leveraging the full power of Intel Xeon Scalable processors. Also, learn how you can rapidly train, deploy, and operationalize AI/ML and big data applications on AWS. This presentation is brought to you by Intel, an APN Partner. Session
AIM204-S - Discovering the value of a cloud data platform In this session, Discover Financial Services and Accenture discuss their work with moving Discover from an on-premises data infrastructure to the AWS Cloud, which offers advanced analytics. With an intelligent data strategy and fully optimized AWS Cloud data solution, Discover is transforming the customer experience and increasing shareholder value. Today, the bank leverages data from many sources—structured and unstructured, streaming and batch—and analyzes the data for insights. This strategy required a bold pivot from a legacy, on-premises architecture to a fully integrated data platform on the cloud. Learn how Discover is also successfully navigating the cultural changes of this type of transformation. This presentation is brought to you by Accenture, an APN Partner. Session Arunashish Majumdar Michael Oppenheim
AIM301-R - [REPEAT] Creating high-quality training datasets with data labeling Amazon SageMaker Ground Truth makes it easy to quickly label high-quality, accurate training datasets. In this workshop, we set up labeling jobs for text and images to help you understand how to make the most of Amazon SageMaker Ground Truth. You learn how to explore and prepare the dataset and label it with object bounding boxes. Then, we use Amazon SageMaker to train a Single Shot MultiBox Detector (SSD) object-detection model based on the labeled dataset, use hyperparameter optimization to find the best model for deployment, and deploy the model to an endpoint for use in an application. Workshop
AIM301-R1 - [REPEAT 1] Creating high-quality training datasets with data labeling Amazon SageMaker Ground Truth makes it easy to quickly label high-quality, accurate training datasets. In this workshop, we set up labeling jobs for text and images to help you understand how to make the most of Amazon SageMaker Ground Truth. You learn how to explore and prepare the dataset and label it with object bounding boxes. Then, we use Amazon SageMaker to train a Single Shot MultiBox Detector (SSD) object-detection model based on the labeled dataset, use hyperparameter optimization to find the best model for deployment, and deploy the model to an endpoint for use in an application. Workshop
AIM302-R - [REPEAT] Create a Q&A bot with Amazon Lex and Amazon Alexa A recent poll showed that 44 percent of customers would rather talk to a chatbot than to a human for customer support. In this workshop, we show you how to deploy a question-and-answer bot using two open-source projects: QnABot and Lex-Web-UI. You get started quickly using Amazon Lex, Amazon Alexa, and Amazon Elasticsearch Service (Amazon ES) to provide a conversational chatbot interface. You enhance this solution using AWS Lambda and integrate it with Amazon Connect. Workshop Bob Strahan John Calhoun
AIM302-R1 - [REPEAT 1] Create a Q&A bot with Amazon Lex and Amazon Alexa A recent poll showed that 44 percent of customers would rather talk to a chatbot than to a human for customer support. In this workshop, we show you how to deploy a question-and-answer bot using two open-source projects: QnABot and Lex-Web-UI. You get started quickly using Amazon Lex, Amazon Alexa, and Amazon Elasticsearch Service (Amazon ES) to provide a conversational chatbot interface. You enhance this solution using AWS Lambda and integrate it with Amazon Connect. Workshop John Calhoun Bob Strahan
AIM303-R - [REPEAT] Stop guessing: Use AI to understand customer conversations You don't need to be a data scientist to build an AI application. In this workshop, we show you how to use AWS AI services to build a serverless application that can help you understand your customers. Analyze call-center recordings with the help of automatic speech recognition (ASR), translation, and natural language processing (NLP). Get hands-on by producing your own call recordings using Amazon Connect. In the last step, you set up a processing pipeline to automate transcription and NLP analysis, and run analytics and visualizations on the results. Workshop Boaz Ziniman Dirk Froehner
AIM303-R1 - [REPEAT 1] Stop guessing: Use AI to understand customer conversations You don't need to be a data scientist to build an AI application. In this workshop, we show you how to use AWS AI services to build a serverless application that can help you understand your customers. Analyze call-center recordings with the help of automatic speech recognition (ASR), translation, and natural language processing (NLP). Get hands-on by producing your own call recordings using Amazon Connect. In the last step, you set up a processing pipeline to automate transcription and NLP analysis, and run analytics and visualizations on the results. Workshop Dirk Froehner Boaz Ziniman
AIM304-R - [REPEAT] Build a content-recommendation engine with Amazon Personalize Machine learning is being used increasingly to improve customer engagement by powering personalized product and content recommendations. Amazon Personalize lets you easily build sophisticated personalization capabilities into your applications, using machine learning technology perfected from years of use on Amazon.com. In this workshop, you build your own recommendation engine by providing training data, building a model based on the algorithm of your choice, testing the model by deploying your Amazon Personalize campaign, and integrating it into your own application. Workshop Andrew Kane Luke Hargreaves
AIM304-R1 - [REPEAT 1] Build a content-recommendation engine with Amazon Personalize Machine learning is being used increasingly to improve customer engagement by powering personalized product and content recommendations. Amazon Personalize lets you easily build sophisticated personalization capabilities into your applications, using machine learning technology perfected from years of use on Amazon.com. In this workshop, you build your own recommendation engine by providing training data, building a model based on the algorithm of your choice, testing the model by deploying your Amazon Personalize campaign, and integrating it into your own application. Workshop Andrew Kane Luke Hargreaves
AIM305-R - [REPEAT] Automate content moderation and compliance with AI Brand safety is a major concern as advertising becomes more automated, issues with ad adjacency arise, contracts with brands or celebrities run out, and user-generated content is more prevalent. In this workshop, you learn how to use Amazon Rekognition, Amazon Textract, and Amazon Comprehend to detect inappropriate content for moderation, or improper use of content like logos or celebrity faces for compliance. You leave with a scalable architecture that will save days of manual review in media moderation and compliance workflows. Workshop Niranjan Hira Kashif Imran
AIM305-R1 - [REPEAT 1] Automate content moderation and compliance with AI Brand safety is a major concern as advertising becomes more automated, issues with ad adjacency arise, contracts with brands or celebrities run out, and user-generated content is more prevalent. In this workshop, you learn how to use Amazon Rekognition, Amazon Textract, and Amazon Comprehend to detect inappropriate content for moderation, or improper use of content like logos or celebrity faces for compliance. You leave with a scalable architecture that will save days of manual review in media moderation and compliance workflows. Workshop Kashif Imran Niranjan Hira
AIM306 - Deploying machine learning models in production Amazon SageMaker is a modular service that makes it easy to build, train, and deploy machine learning models. In this session, we dive deep into how to deploy machine learning models in the cloud and on edge devices. Amazon SageMaker lets you deploy your trained model in production with a single click so that you can start generating predictions. We also explain how to reduce inference costs by up to 75 percent using Amazon Elastic Inference, and how to train models once and run them anywhere using Amazon SageMaker Neo. Come away understanding all of your options for model deployment. Session
AIM307 - Amazon SageMaker deep dive: A modular solution for machine learning Amazon SageMaker is a fully managed service that offers developers and data scientists the flexibility to build, train, and deploy machine learning models through modular capabilities. In this session, we dive deep into the technical details of each module so you understand how to label and prepare your data, choose an algorithm, train and optimize the model, and make predictions. We also discuss practical deployments of Amazon SageMaker through real-world customer examples. Session
AIM308 - Build accurate training datasets with Amazon SageMaker Ground Truth Successful machine learning models are built on high-quality training datasets. Typically, the task of labeling is distributed across a large number of humans, adding significant overhead and cost. In this session, learn how Amazon SageMaker Ground Truth reduces cost and complexity using a machine learning technique called active learning to label datasets. Active learning reduces the time and manual effort required to do data labeling, by continuously training machine learning algorithms based on labels from humans. Session
AIM310-S - Why observability requires the marriage of AI, metrics, and logs The new digital world presents great opportunity as workloads move to the cloud and containers and companies benefit from serverless computing and an agile application delivery chain. However, these opportunities come with significant challenges. Site reliability engineers have been tasked with knitting together disparate platforms to build an observable stack, which is imperative for early detection of service degradation issues. We demonstrate a novel alternative that combines metrics, logs, and alerts into a comprehensive AIOps approach. Learn how to deliver an AI-enabled service that provides instant observability of your cloud application stack and how to combine logs and metrics into a single pane of glass. This presentation is brought to you by Moogsoft, an APN Partner. Session Phil Tee
AIM401-R - [REPEAT] Deep learning with TensorFlow The TensorFlow deep-learning framework has broad support in areas such as computer vision, natural language understanding, and speech translation. You can get started with a fully managed TensorFlow experience using Amazon SageMaker, a service designed to build, train, and deploy machine learning models at scale. In this workshop, we build, train, and deploy a computer-vision model using key TensorFlow features, including distributed training with Horovod, training on pipe mode datasets, and monitoring with TensorBoard. Workshop
AIM401-R1 - [REPEAT 1] Deep learning with TensorFlow The TensorFlow deep-learning framework has broad support in areas such as computer vision, natural language understanding, and speech translation. You can get started with a fully managed TensorFlow experience using Amazon SageMaker, a service designed to build, train, and deploy machine learning models at scale. In this workshop, we build, train, and deploy a computer-vision model using key TensorFlow features, including distributed training with Horovod, training on pipe mode datasets, and monitoring with TensorBoard. Workshop
AIM402-R - [REPEAT] Deep learning with PyTorch PyTorch is a deep-learning framework that is becoming popular, especially for rapid prototyping of new models. You can get started easily with PyTorch using Amazon SageMaker, a fully managed service, to build, train, and deploy machine learning models at scale. In this workshop, we build a natural-language-processing model to analyze text. Workshop
AIM402-R1 - [REPEAT 1] Deep learning with PyTorch PyTorch is a deep-learning framework that is becoming popular, especially for rapid prototyping of new models. You can get started easily with PyTorch using Amazon SageMaker, a fully managed service, to build, train, and deploy machine learning models at scale. In this workshop, we build a natural-language-processing model to analyze text. Workshop
AIM403-R - [REPEAT] Deep learning with Apache MXNet In this workshop, learn how to get started with the Apache MXNet deep learning framework using Amazon SageMaker, a fully managed service, to build, train, and deploy machine learning models at scale. Learn how to build a computer-vision model using MXNet to extract insights from an image dataset. Once the model is built, learn how to quickly train it to get the best possible results and then easily deploy it to production using Amazon SageMaker. Workshop
AIM403-R1 - [REPEAT 1] Deep learning with Apache MXNet In this workshop, learn how to get started with the Apache MXNet deep learning framework using Amazon SageMaker, a fully managed service, to build, train, and deploy machine learning models at scale. Learn how to build a computer-vision model using MXNet to extract insights from an image dataset. Once the model is built, learn how to quickly train it to get the best possible results and then easily deploy it to production using Amazon SageMaker. Workshop
AIM404-R - [REPEAT] Reinforcement learning with Amazon SageMaker Reinforcement learning (RL) is used to build sophisticated models without the need for pre-labeled training data. In this workshop, you learn how to use the built-in, fully managed RL algorithms that are part of Amazon SageMaker RL. Amazon SageMaker supports RL in multiple frameworks, including TensorFlow and MXNet, as well as in custom-developed frameworks designed from the ground up for reinforcement learning, such as Intel Coach and Ray RLlib. Also, use Amazon SageMaker RL to train in using virtual 3D environments that are built in Amazon Sumerian and AWS RoboMaker. Workshop
AIM404-R1 - [REPEAT 1] Reinforcement learning with Amazon SageMaker Reinforcement learning (RL) is used to build sophisticated models without the need for pre-labeled training data. In this workshop, you learn how to use the built-in, fully managed RL algorithms that are part of Amazon SageMaker RL. Amazon SageMaker supports RL in multiple frameworks, including TensorFlow and MXNet, as well as in custom-developed frameworks designed from the ground up for reinforcement learning, such as Intel Coach and Ray RLlib. Also, use Amazon SageMaker RL to train in using virtual 3D environments that are built in Amazon Sumerian and AWS RoboMaker. Workshop
AIM405-R - [REPEAT] Start using computer vision with AWS DeepLens If you're new to deep learning, this workshop is for you. Learn how to build and deploy computer-vision models using the AWS DeepLens deep-learning-enabled video camera. Also learn how to build a machine learning application and a model from scratch using Amazon SageMaker. Finally, learn to extend that model to Amazon SageMaker to build an end-to-end AI application. Workshop
AIM405-R1 - [REPEAT 1] Start using computer vision with AWS DeepLens If you're new to deep learning, this workshop is for you. Learn how to build and deploy computer-vision models using the AWS DeepLens deep-learning-enabled video camera. Also learn how to build a machine learning application and a model from scratch using Amazon SageMaker. Finally, learn to extend that model to Amazon SageMaker to build an end-to-end AI application. Workshop
ALX201-R - [REPEAT] How developers can build natural, extensible voice conversations Alexa dialogue technology allows you to build multi-turn conversations that sound natural to customers; it also allows you to be a part of broader customer experiences such as seeking a recommendation or planning a night out. This session walks you through implementing Alexa dialogue technology to create rich conversational experiences. Session Jeff Blankenburg Justin Jefferess Josey Sandoval
ALX201-R1 - [REPEAT 1] How developers can build natural, extensible voice conversations Alexa dialogue technology allows you to build multi-turn conversations that sound natural to customers; it also allows you to be a part of broader customer experiences such as seeking a recommendation or planning a night out. This session walks you through implementing Alexa dialogue technology to create rich conversational experiences. Session Justin Jefferess Josey Sandoval Jeff Blankenburg
ALX201-R2 - [REPEAT 2] How developers can build natural, extensible voice conversations Alexa dialogue technology allows you to build multi-turn conversations that sound natural to customers; it also allows you to be a part of broader customer experiences such as seeking a recommendation or planning a night out. This session walks you through implementing Alexa dialogue technology to create rich conversational experiences. Session Justin Jefferess
ALX202-R - [REPEAT] Alexa, what can I do now? Every year, the Alexa Skills Kit (ASK) grows in capabilities and features. With more than 100,000 published skills, ASK continues to push forward, making new opportunities for developers who want to build engaging voice experiences with Amazon Alexa. In this session, we discuss the latest trends in conversational artificial intelligence, highlight some of the most innovative skills, and provide an overview of everything that has been released in the past year for the ASK, including expansion to Hindi, natural language evaluation, and leaderboards. Session Jeff Blankenburg
ALX202-R1 - [REPEAT 1] Alexa, what can I do now? Every year, the Alexa Skills Kit (ASK) grows in capabilities and features. With more than 100,000 published skills, ASK continues to push forward, making new opportunities for developers who want to build engaging voice experiences with Amazon Alexa. In this session, we discuss the latest trends in conversational artificial intelligence, highlight some of the most innovative skills, and provide an overview of everything that has been released in the past year for the ASK, including expansion to Hindi, natural language evaluation, and leaderboards. Session Jeff Blankenburg
ALX203-R - [REPEAT] Go from 0 to 60 with Alexa Connect Kit Are you a device maker looking to build voice-controlled products without dealing with the complexity of managing cloud services, writing an Alexa skill, or developing complex networking and security firmware? Introducing Alexa Connect Kit (ACK)—a combination of hardware and software that allows you to quickly develop smart products that customers will love with built-in features like Wi-Fi simple setup, Amazon Dash replenishment, and fleet management with over-the-air (OTA) updates. In this session, we turn a simple fan into an Alexa-controlled smart device in less than an hour. Session Yow-hann Lee
ALX203-R1 - [REPEAT 1] Go from 0 to 60 with Alexa Connect Kit Are you a device maker looking to build voice-controlled products without dealing with the complexity of managing cloud services, writing an Alexa skill, or developing complex networking and security firmware? Introducing Alexa Connect Kit (ACK)—a combination of hardware and software that allows you to quickly develop smart products that customers will love with built-in features like Wi-Fi simple setup, Amazon Dash replenishment, and fleet management with over-the-air (OTA) updates. In this session, we turn a simple fan into an Alexa-controlled smart device in less than an hour. Session
ALX301-R - [REPEAT] Build next-generation voice-enabled devices with Alexa Every day, we see more and more connected items in our world, from microwaves to cars. We are headed toward a future where we’re surrounded by devices that can communicate with the world around them. In this hands-on session, you learn how to add custom voice control to your connected devices with Alexa. Leave your laptop behind, but bring your big ideas—we supply the hardware. You create an Alexa built-in prototype, AWS IoT “thing,” and your own Alexa skill, all on a Raspberry Pi. Leave this session with your own voice-enabled prototype that interfaces with whatever inputs and outputs you can imagine. Workshop
ALX301-R1 - [REPEAT 1] Build next-generation voice-enabled devices with Alexa Every day, we see more and more connected items in our world, from microwaves to cars. We are headed toward a future where we’re surrounded by devices that can communicate with the world around them. In this hands-on session, you learn how to add custom voice control to your connected devices with Alexa. Leave your laptop behind, but bring your big ideas—we supply the hardware. You create an Alexa built-in prototype, AWS IoT “thing,” and your own Alexa skill, all on a Raspberry Pi. Leave this session with your own voice-enabled prototype that interfaces with whatever inputs and outputs you can imagine. Workshop
ALX302-R - [REPEAT] Build an engaging Alexa skill with Cake Walk An engaging Alexa skill is built upon a well-thought-out design. A major part of designing the experience is mimicking an engaging human conversational partner. In this workshop, learn how to build Cake Walk, a simple skill that delights customers by wishing them happy birthday. Learn how to start with the “happy path” and use situational design for the interaction, then implement the skill. Use auto-delegation to collect information and Amazon S3 to give your skill memory. Finally, learn how to use the Alexa Settings API to look up the time zone of the device to accurately determine the customer’s birthday. Workshop Justin Jefferess Anna Van Brookhoven
ALX302-R1 - [REPEAT 1] Build an engaging Alexa skill with Cake Walk An engaging Alexa skill is built upon a well-thought-out design. A major part of designing the experience is mimicking an engaging human conversational partner. In this workshop, learn how to build Cake Walk, a simple skill that delights customers by wishing them happy birthday. Learn how to start with the “happy path” and use situational design for the interaction, then implement the skill. Use auto-delegation to collect information and Amazon S3 to give your skill memory. Finally, learn how to use the Alexa Settings API to look up the time zone of the device to accurately determine the customer’s birthday. Workshop Justin Jefferess Anna Van Brookhoven
ALX303 - Learn situational design with Cake Walk Design is an important part of building voice-first user interfaces. A major part of designing the experience is mimicking a human conversational partner. Even simple skills can benefit from a well-thought-out design. In this workshop you learn about situational design and how we used it to build Cake Walk, a simple skill that delights customers by wishing them happy birthday. You get the “happy path” script and walk through a series of activities to teach you how to build a situational design deck. Chalk Talk Joanna Tracy Anna Van Brookhoven
ALX304-R - [REPEAT] Build and monetize an Alexa skill using in-skill purchasing Experienced Alexa Skills Kit builders only; must bring your laptop! In-skill purchasing (ISP) lets you sell digital content like game features, interactive stories, or hints in a trivia skill. Customers may ask to shop products, buy products by name, or agree to purchase suggestions while they interact with a skill. In this workshop, learn how to build a skill with premium content that can be unlocked via subscriptions, entitlements, and consumables. Learn the complete process, from building to testing to publishing, and monetize your skills to tap into a larger customer base, generating revenue to grow your global voice business. Builders Session German Viscuso
ALX304-R1 - [REPEAT 1] Build and monetize an Alexa skill using in-skill purchasing Experienced Alexa Skills Kit builders only; must bring your laptop! In-skill purchasing (ISP) lets you sell digital content like game features, interactive stories, or hints in a trivia skill. Customers may ask to shop products, buy products by name, or agree to purchase suggestions while they interact with a skill. In this workshop, learn how to build a skill with premium content that can be unlocked via subscriptions, entitlements, and consumables. Learn the complete process, from building to testing to publishing, and monetize your skills to tap into a larger customer base, generating revenue to grow your global voice business. Builders Session Andrea Muttoni
ALX304-R2 - [REPEAT 2] Build and monetize an Alexa skill using in-skill purchasing Experienced Alexa Skills Kit builders only; must bring your laptop! In-skill purchasing (ISP) lets you sell digital content like game features, interactive stories, or hints in a trivia skill. Customers may ask to shop products, buy products by name, or agree to purchase suggestions while they interact with a skill. In this workshop, learn how to build a skill with premium content that can be unlocked via subscriptions, entitlements, and consumables. Learn the complete process, from building to testing to publishing, and monetize your skills to tap into a larger customer base, generating revenue to grow your global voice business. Builders Session Andrea Muttoni
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