歡迎參加 AWS Innovate Online Conference –
AI and Machine Learning Edition

 2020 年 2 月 19 日

歡迎參加 AWS Innovate Online Conference – AI and Machine Learning Edition 旨在激發並助力您加速創新、輕鬆擴展並開拓新的可能性。您可自主題演講中,聽到 AWS 首席架構師 Glenn GoreAWS 新興技術主管 Olivier Klein 以及 AWS 首席架構師 Dean Samuels 了解最新的 AI 和 Machine Learning 相關資訊。

除此以外,還能選擇進入【繁體中文-國語】場次,深入瞭解 AWS 專家提供的四段中文內容,探索關鍵概念、使用案例、最佳實務、即時示範和即時問答,了解其他組織如何使用 AI 和 Machine Learning,並憑藉為您的組織實作這些專案的能力輕鬆制勝。

了解 AWS 客戶如何使用機器學習來改善醫療保健品質、對抗人口販賣、提供更好的客戶服務,以及保護您免於詐騙的危害。他們運用這些範圍最廣、程度最深的機器學習和 AI 服務,取得新的見解、開創新的效率,並進行更準確的預測。這也是為什麼有超過 10,000 位客戶選擇採用 AWS 機器學習功能的原因。


的客戶選用 AWS 提供的機器學習功能


的雲端深度學習專案在 AWS 上執行


的雲端 TensorFlow 專案在 AWS 上執行


的雲端 PyTorch 專案在 AWS 上進行


  • 繁體中文-國語場次
    • 展望未來:數據與機器學習
    • Level 100:【用數據及機器學習開創未來】

      數據和機器學習可以解決那些問題?人類如何用這兩者開創跟嶄新美好的未來?本影片將由AWS首席架構 Glenn Gore 口述,娓娓道來AI/ML的展望,此段内容將以英文發音,搭配繁體中文字幕播出。

    • 建置、訓練、部署ML模型
    • Level: 100~200: 【AWS DeepRacer原理原則及致勝法則分享】

      透過這段演講,AWS的專家將帶大家瞭解DeepRacer的背後機器學習的原理,以及贏得DeepRacer Challenge的方針!

    • Amazon 的創新
    • Level 200:【AWS AI 新服務探索】

      這段演講由 AWS 解決方案架構師 Jason Wang主講,我們將探討如何在沒有機器學習背景下,打造AI 應用程式。

    • AI未來發展
    • Level 200~300:【深入淺出 AWS AI】  

      這段演講由AWS 機器學習領域專家 Young Yang主講,討論組織如何加速人工智慧解決方案落地以及掌握未來AI發展,演講中將介紹 AutoPilot, SageMaker Studio, Rekognition Custom Labeling等服務。


  • 英語場次
    • Innovation at Amazon
    • Create tomorrow with data and machine learning (Level 100)

      Whether it is helping online shoppers automate repeat purchases, creating advanced real-time recommendations for online gamers, or accelerating new product development, businesses today are increasingly recognizing the value of collecting real time and historical data and using machine learning technology to innovate faster for customers. In this session, Glenn Gore, Worldwide Lead Solutions Architect, AWS, explores how businesses such as Amazon Retail, Amazon Alexa, and Amazon Robotics use data and machine learning to innovate for customers.

      Glenn Gore, Chief Architect, AWS
      Tye Brady, Chief Technologist, Amazon Robotics
      Manoj Sindhwani, VP, Alexa Speech
      Jenny Freshwater, Director of Supply Chain Forecasting & Capacity Planning, Amazon
      Mike Vogelsong, Senior Machine Learning Scientist, Amazon

    • Accelerate your ML journey
    • Accelerating machine learning: Your role in the journey (Level 100)

      Artificial intelligence and machine learning (ML) hold the promise of transforming industries, increasing efficiencies, and driving innovation. Executives play important roles in accelerating the ML journey, but while many are prioritizing ML, going from idea to implementation can be daunting and can stop companies before they begin. In this session, we discuss ML implementation challenges and explain how AWS customers have been successful in introducing ML to help achieve their business goals. We share how customers are working with AWS to align teams, drive ML excitement, provide developers with the right technical education, and identify appropriate use cases to start moving from idea to production.

      Speaker: Joel Minnick, Head of AI Product Marketing, AWS

      Learn machine learning like an Amazonian (Level 100)

      Machine learning (ML) and artificial intelligence are stealing headlines and sparking the imaginations of individuals working in big organizations and the startup community. Yet, one of the biggest barriers to the adoption of ML is finding and cultivating trained talent. In this session, learn how AWS Training and Certification is building on the curriculum used to train thousands of Amazon’s developers and providing resources to help make your ML vision a reality.

      Speaker: Elly Juniper, Senior Technical Business Development Manager, AWS

      Accelerate ML projects with ML and data services from AWS Marketplace (Level 200)

      Companies spend significant time developing, searching for, and evaluating algorithms and models to solve business problems using machine learning (ML). The AWS Marketplace has hundreds of algorithms and model packages that can be deployed quickly onto Amazon SageMaker. This session provides an introduction to the AWS Marketplace and how it can help you accelerate your ML projects by using third-party models and algorithms.

      Speaker: Kanchan Waikar, Senior Partner Solutions Architect, AWS

    • AI/ML Fundamentals
    • Getting started with machine learning and improve productivity in a fully integrated development environment (Level 100)

      Amazon SageMaker is a fully managed, modular service that enables developers and data scientists to build and scale machine learning (ML) solutions. With it, you can directly deploy ML solutions into production-ready hosted environments. Come learn about Amazon SageMaker Studio, an integrated development environment (IDE) that lets you build, train, debug, deploy, and monitor ML models. This service provides the tools needed to accelerate the process of taking your models from experimentation to production and to boost productivity. In a unified visual interface, you can write and execute code in Jupyter notebooks and monitor the performance of your predictions, as well as track and debug ML experiments.

      Speaker: Kapil Pendse, Senior Solutions Architect, AWS

      Meeting security and compliance objectives when using Amazon SageMaker (Level 200)

      Amazon SageMaker is a fully managed machine learning (ML) service that data scientists and developers use to build predictive and analytical models with their data. Datasets used for research may contain sensitive information, such as medical records or intellectual property, that must be protected. In this session, we guide you through the steps needed to ensure security and compliance when using Amazon SageMaker, including using data encryption, identity and access management, logging and monitoring, compliance validation, and infrastructure security.

      Speaker: Michael Stringer, Senior Solutions Architect, AWS

      Building machine learning workflows with Kubernetes and Amazon SageMaker (Level 200)

      Until recently, data scientists had to spend significant time performing operational tasks, such as ensuring that frameworks, runtimes, and drivers for CPUs and GPUs worked well together. They also needed to design and build machine learning (ML) pipelines to orchestrate complex workflows for deploying ML models in production. In this session, we dive into Amazon SageMaker and container technologies and discuss how easy it is to integrate tasks such as model training and deployment into Kubernetes and Kubeflow-based ML pipelines. Further, we show how the new Amazon SageMaker Operators for Kubernetes makes it easier to use Kubernetes to train, tune, and deploy ML models in Amazon SageMaker.

      Speaker: Arun Balaji, Partner Solutions Architect, AISPL

      Evolution of personalization and recommendation for video workflows (Level 200)

      Personalizing the user experience is proven to increase discoverability, user engagement and satisfaction, and revenue. However, many AWS customers find personalization difficult to do correctly. Effective recommender systems require solving multiple hard problems, including constantly changing user behavior, new catalog items (cold start), and more. In this session, we cover some of the most common methods of personalization and recommendation. Additionally, the Amazon Prime Video team shares the evolution of its recommendation system and the real-world challenges that it faced when building recommendation systems at scale.

      Speaker: Liam Morrison, Principal Specialist Solutions Architect, AWS 

      Scale machine learning from zero to millions of users (Level 200)

      Data scientists and machine learning (ML) engineers use a variety of tools that make it easy to start everyday tasks. But as models become more complex and datasets become larger, training time and prediction latency become significant concerns. In this session, we show you how to scale ML workloads using AWS services, including AWS Deep Learning AMIs and containers, Amazon ECS, Amazon EKS, and AWS Fargate. We also discuss the relative advantages of these services, and we run some interesting demonstrations.

      Speaker: Praveen Jayakumar, Solutions Architect, AISPL

    • Build, train and deploy ML models
    • Automate machine learning: From debugging deep learning to detecting model drift in production (Level 300)

      Machine learning (ML) involves more than just training models; developers need to debug these deep learning models as well as monitor their performance in production so that they serve their intended business purpose. However, models can become outdated as the nature of data changes, causing model drift in production and generating irrelevant results. This type of model degradation tends to go undetected. In this session, we cover how to help radically reduce troubleshooting time in building and training high-quality ML models and how to identify and detect drift in your ML model post-deployment.

      Speaker: Aparna Elangovan, Prototype Engineer, AI/ML, AWS

      Auto-create machine learning models with full visibility using Amazon SageMaker Autopilot (Level 300)

      If you think that machine learning (ML) sounds like a lot of experimental trial-and-error work, you are absolutely right. Building ML models has traditionally required a binary choice: either having deep expertise in-house, which is rare, or using an automated approach which gives little visibility into how the model was created. In this session, find out how you can simply call an API and get the job done, and learn how Amazon SageMaker Autopilot allows you to automatically build ML models without compromises.

      Speaker: Tapan Hoskeri, Solutions Architect, AISPL

      Machine learning deployment on AWS: Best practices to decide what, where, and how (Level 300)

      Putting ML solutions in production requires knowing the what, where, and how of deploying ML models. One needs to know what the model is (resource requirements in production) and what the business context is (input workload, output consumers, batch vs. real-time inference, etc.); where to deploy (cloud or edge, based on cost-effectiveness, fulfillment of business SLAs, etc.); and how to deploy (based on ease of deployment, scaling, A/B testing, etc.). Working backwards from these customer questions, AWS offers the broadest and deepest range of ML deployment options. This session covers these options and how they address the above questions from a best-practices perspective.

      Speaker: Sujoy Roy, Senior Data Scientist, AWS

      Accelerate the building of deep learning applications (Level 300)

      The AWS Deep Learning AMIs provide machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud, at any scale. Whether Amazon EC2 GPU or CPU instances are used, with AWS Deep Learning AMIs, users pay only for the AWS resources needed to store and run their applications. In this session, learn how to quickly launch Amazon EC2 instances preinstalled with popular deep learning frameworks and interfaces—such as TensorFlow, PyTorch, and Apache MXNet, Chainer, Gluon, Horovod, and Keras—to train sophisticated, custom AI models; experiment with new algorithms; or learn new skills and techniques. Whether you need Amazon EC2 GPU or CPU instances, there is no additional charge for the Deep Learning AMIs – you only pay for the AWS resources needed to store and run your applications.

      Speaker: Pedro Paez, Specialist Solutions Architect, AWS

    • AI Services & Applications
    • Amazon Fraud Detector: Detect more online fraud faster (Level 200)

      Globally each year, tens of billions of dollars are lost to online fraud. Companies conducting business online are especially prone to attacks from bad actors, who often exploit tactics such as creating fake accounts and making payments with stolen credit cards. Companies typically use fraud detection applications to identify fraudsters and stop them before they cause costly business disruptions. This session details how to implement a customized fraud detection solution for online activities, using machine learning to proactively identify and implement changes in the protection of your company and customers.

      Speaker: Eric Greene, AI Specialist Solutions Architect, AWS

      Simplify and accelerate time-series forecasting and real-time personalization (Level 200)

      Deploying custom machine learning (ML) models to solve complex business challenges does not have to be hard. Based on ML technology perfected through years of use on Amazon.com, Amazon Forecast and Amazon Personalize enable developers with no prior ML experience to easily build accurate forecasting and sophisticated personalization capabilities into their applications. In this session, we show you how these two services, using a new automating process, AutoML, create individualized recommendations for customers and deliver highly accurate forecasts. Both services run on fully managed infrastructure and provide easy-to-use recipes that deliver high-quality models even if you have little ML experience.

      Speaker: Anand Iyer, Enterprise Solutions Architect, AISPL

      Amazon Kendra: Reinvent enterprise search and interact with data using AI (Level 300)

      How can you get the most accurate and specific result to a search query when the answer is hidden within various enterprise information systems? In this session, we show you how to use Amazon Kendra, an enterprise search solution that provides straightforward answers to specific search queries, such as, “How much is the cash reward on the corporate credit card?” Learn how Amazon Kendra can improve cross-team knowledge sharing, increase sales, and enhance customer support services. Also, discover how this new service makes it easier for customers to find the information they need.

      Speaker: Will Badr, Senior AI/ML Solutions Architect, AWS

      Breaking language barriers with AI (Level 300)

      Amazon brings natural language processing, speech recognition, text-to-speech capabilities, and machine translation within the reach of every developer. API-driven application services enable data scientists and developers to easily add pre-built artificial intelligence functionality into their applications and automate workflows. In this session, learn how to build the next generation of intelligent applications that hear, speak, and understand the world around us.

      Speaker: Sara van de Moosdijk, Partner Solutions Architect, AWS

      Machine learning powered analytics for contact centers (Level 300)

      Today, most contact center analytics are based on phone switch activity or CRM call notes that are recorded by the contact center agent. However, these analytics typically lack insights into the actual conversations between agents and customers. In this session, we explain how Amazon Connect analyzes the nuances of contact center conversations, including those in different languages and those that have custom vocabularies. Discover how Contact Lens for Amazon Connect enables customer service supervisors to conduct fast, full-text searches on call and chat transcripts to quickly troubleshoot customer issues. Learn how this service uses call and chat-specific analytics, including sentiment analysis and silence detection, to improve agents’ performance.

      Speaker: Sumit Patel, Enterprise Solutions Architect, AWS

    • AI/ML Services and Devices
    • AWS DeepRacer: Train, evaluate and tune your reinforcement learning model (Level 300)

      In this session, we introduce the basics of reinforcement learning and show you how to apply it train your own autonomous vehicle models. You also learn how to test them in a virtual car racing scenario powered by AWS DeepRacer. Learn about the single-car time-trial format and the dual-car head-to-head racing challenges in the AWS DeepRacer 3D racing simulator. At the end of this session, you will be able to participate in the AWS DeepRacer League, where you can compete for prizes and meet other machine learning enthusiasts.

      Speaker: Gabe Hollombe, Senior Developer Advocate, AWS

      Amazon CodeGuru: Automate code reviews and application performance recommendations (Level 400)

      It can be difficult to detect certain types of code issues and identify the most expensive lines of code without performance engineering expertise, even for the most seasoned engineers. CodeGuru is a new machine learning service that enables you to discover code issues quickly and improve application performance. Discover how CodeGuru works in this session. We show you how it reviews Java code in your GitHub and AWS CodeCommit source code repositories, profiles your applications, and searches for optimizations, even in production. Finally, learn how CodeGuru provides intelligent recommendations so that you can take immediate action to fix code issues or improve inefficiencies.

      Pedro Paez, Specialist Solutions Architect, AWS
      Donnie Prakoso, Senior Developer Advocate, AWS

      Large scale image and video analysis with Amazon Rekognition (Level 200)

      Companies are using computer vision to understand the content and context of their images and videos at scale. This session provides an overview of Amazon Rekognition Custom Labels, a new feature of Amazon Rekognition that enables customers to build their own machine learning–based image analysis capabilities to detect unique objects and scenes that are relevant to their business needs. Join this session to learn how to use Amazon Rekognition Custom Labels for your own business needs. We also share how Amazon Augmented AI (Amazon A2I) makes it easy for human review of ML predictions and learn how worker safety is enabled with AWS DeepLens and Amazon Rekognition.

      Speaker: Imran Kashif, Senior Solutions Architect, AWS

      Get started with generative AI using AWS DeepComposer (Level 300)

      In this session, learn how to use generative AI to create music with AWS DeepComposer. Get hands on with the world's first machine learning enabled musical keyboard and record a melody. Then, learn how to send it to the cloud to generate an accompaniment. We demonstrate how to import the MIDI files into a digital audio workstation to create the final arrangement.

      Speaker: Julian Bright, AI Specialist Solutions Architect, AWS

    • Hands-on Labs
    • Reinforcement learning 

      In this lab, learn how to build reinforcement learning (RL) models using Amazon SageMaker RL, which includes prebuilt RL libraries and algorithms that help you achieve faster turnaround times and improve the results of your RL experiments.

      Customer churn prediction

      In this lab, learn how identifying unhappy customers early provides you with the opportunity to incentivize them to stay and helps decrease customer churn. In this lab, we explain how to use machine learning (ML) to predict customer churn. We also discuss how to incorporate the costs associated with prediction mistakes to determine the financial outcome of using ML.

      Virtual contact center

      This lab explains how to build a contact center using Amazon Connect and Amazon Lex. Learn how to match intent based on your input and provide greater flexibility for customers who interact with contact centers.

      AWS DeepRacer

      Get ready to race by building your own AWS DeepRacer reinforcement learning (RL) model. AWS DeepRacer is an integrated learning system for users of all levels that allows you to explore RL and experiment with building autonomous driving applications. In this lab, you get hands-on with creating, training, and tuning your RL model.

      Build, train, and debug machine learning models

      In this lab, we show the different aspects of the machine learning (ML) workflow for building, training, and deploying a model using all the capabilities of Amazon SageMaker. We also discuss how Amazon SageMaker removes the heavy lifting from each step of the ML workflow. Come learn how to build, train, debug, monitor, and deploy your ML models.

    • Builders' Zone Demo
    • Virtual rap battles

      AI is quickly making inroads into the entertainment industry, helping create music loops, drum track samples, and even entire heavy metal albums. In this session, learn how to use Amazon SageMaker to build and train a lyrics-generation model and host the model as a real-time API. We show you how to create two virtual rappers using Amazon Sumerian and have them trade rhymes with each other. For a control panel, we use Amazon Comprehend, which detects the key phrase from the first rapper and passes it to the second one and enables replies that are specific to the topic of the first rapper’s rhyme.

      Speaker: Tapan Hoskeri, Senior Solutions Architect, AISPL

      ML soccer bots

      AWS AI/ML and IoT services can be used together for more than solving edge use cases with limited internet connectivity. In this demo, watch as they make two bots smart enough to compete with each other—finding, chasing, and striking a soccer ball. These soccer bots are built using a single-board computer with a Pi camera as the only sensor. They come to life with a combination of AWS services like Amazon SageMaker and AWS IoT Greengrass Core. Observe how they each draw inferences locally from their vision data and make quick decisions using the ML models deployed on their single-board computer.

      Arun Balaji, Partner Solution Architect, AISPL
      Ananth Balasubramanyam, Solutions Architect, AISPL

      Smart skittles sorter

      Modern connected industrial environments rely on various types of sensors and automation tools. Many such environments use advanced technologies like computer vision and machine learning for automation. In this session, we demonstrate how to use Amazon SageMaker and AWS IoT to deploy and operate a mechanism that sorts Skittles by color. Learn how to integrate, operate, and monitor industrial automation that consists of Raspberry Pi connected to a camera, relays, and motors and that uses AWS IoT and Amazon SageMaker.

      Speaker: Vijay Menon, Solutions Architect, AISPL

      Image classification with AWS DeepLens

      Learn how to build a custom deep learning image classification model with AWS DeepLens, Amazon SageMaker, AWS IoT Greengrass, and other AWS services. The model can be used for a variety of purposes, including artistic style transfer, facial recognition, and license plate and other object detection.

      Speaker: Ramine Tinati, Senior AI/ML Advocate, AWS

      Underwater garbage detection

      With an estimated 8 million metric tons of trash deposited into oceans each year, there are now close to 500 dead zones, where most marine life cannot survive, globally covering more than 245,000 square kilometers, equivalent to the area of the UK. Clearing this trash is a massive job requiring first knowing exactly where the trash is located. This demo shows how to use machine learning to detect trash underwater, mapping it to its location. We use services like Amazon SageMaker, Amazon Elasticsearch Service, and AWS IoT to run this model at the edge with TensorFlow and an NVIDIA Jetson AGX Xavier Developer Kit.

      Kapil Pendse, Solutions Architect, AWS
      Janos Schwellach, Solutions Architect, AWS

      AI/ML blackjack challenge

      This AI/ML blackjack challenge demo showcases Amazon SageMaker, AWS IoT, and Serverless, using edge compute and cameras. Learn how these services in combination detect playing card rank and suit using computer vision, displaying results in real time, and provide player guidance recommendations and probabilities for the game of blackjack.

      Speaker: Ramine Tinati, Senior AI/ML Advocate, AWS

      Omni channel contact center

      The multichannel experience increasingly forms the core of the business strategy that organizations adopt to connect with their customers. While platforms such as web, mobile, and phone help companies engage and connect with their customers, in most cases, the customer still lacks a seamless experience and consistent messaging across these channels. This demo shows you how to build an omni-channel solution to serve customers in a way that creates an integrated and cohesive customer experience. Using the powerful conversational natural language capabilities of Amazon Lex, see how easily you can build voice-enabled chatbot experiences for customer service.

      Speaker: Donnie Prakoso, Senior Developer Advocate, AWS

      Intelligent car damage assessor

      When a motor vehicle insurance policyholder is involved in an accident, the claims agent ideally, instead of asking that customer to answer questions in a moment of stress, would ask them to simply take a few images of their vehicle and upload them into the customer’s mobile application. In this session, learn how to harness the power of machine learning to provide customers a hassle-free way to claim insurance damages—we cover training a machine learning model to assess damage to vehicles by simply analyzing uploaded images from customers so that you don’t have to rely on numerous subjective questions.

      Speaker: Anand Iyer, Principal Solutions Architect, AISPL 

      AI worker safety system

      Learn to use AWS DeepLens and Amazon Rekognition to build an application that helps identify if a person at a construction site is wearing the right safety gear, in this case, a hard hat. In this lab, we show how you can create and deploy an object detection project to AWS DeepLens, modify the AWS DeepLens object detection inference Lambda function to detect persons and upload the frame to Amazon S3, create a Lambda function to identify those who are not wearing safety hats and analyze the results using AWS IoT , Amazon CloudWatch and a web dashboard.

      Speaker: Imran Kashif, Senior Solutions Architect, AWS

  • 香港
  • 香港時間
    中午 12:30 - 下午 4:30
  • 台灣
  • 台灣時間
    中午 12:30 - 下午 4:30
等級 100
專題講座重點概述 AWS 服務和功能,並假設與會者為該主題的新手。
等級 200
等級 300
等級 400


Glenn Gore
AWS 首席架構師 Glenn Gore

作為 AWS 的首席架構師,Glenn 負責建立架構最佳實務,並與客戶攜手合作,探索如何運用雲端和創新來轉變自己的業務或顛覆新市場。

Glenn 之前曾在 AWS 擔任各種職務,最近作為亞太區域和歐洲、中東和非洲的架構主管,他將負責這兩個快速增長區域的區域團隊管理工作。Glenn 是一位實作技術專家,在技術行業擁有 20 多年的豐富經驗。在加入 AWS 之前,Glenn 是 WebCentral 的技術長,他在該公司負責為客戶開發高度可擴展的 Web 平台和大數據系統。此外,他還曾在 OzEmail 和全球最大的網路供應商 UUNET 中擔任過職務。

Olivier Klein
AWS 新興技術主管 Olivier Klein

Olivier 是一位實作技術專家,在行業中擁有超過 10 年的豐富經驗,並一直在 AWS 整個亞太和歐洲區域工作,以協助客戶建立彈性、可擴展、安全且經濟高效的應用程式,並建立創新、資料驅動型業務模型。他在以下方面提供諮詢建議︰人工智慧、機器學習和物聯網領域中的新興技術如何助力打造新產品,從而讓現有程序更有效率,提供整體業務洞見,以及充分利用消費者的全新互動通道。此外,他還積極協助客戶建置讓 IT 基礎架構和服務支出與收入模型保持一致的平台,從而有效減少浪費,並顛覆此前數十年中執行的產品開發程序。

Dean Samuels
AWS 首席架構師 Dean Samuels

Dean 具有 IT 基礎架構背景,在基礎架構虛擬化和自動化方面擁有豐富的經驗。過去五年間,他一直在 AWS 任職,並有機會與各種規模和行業的企業合作,主要是在澳洲和紐西蘭,以及整個亞太區域。Dean 致力於協助客戶設計、實作和最佳化用於公共雲端的應用程式環境,以使其變得更具創新性、敏捷性和安全性。Dean 一方面確實擁有涵蓋運算、儲存、網路和安全的強大 IT 基礎架構背景,另一方面他非常專注以更具合作性和整合的方式,將 IT 營運和軟體開發實務結合在一起。


1.AWS Innovate 在何處舉辦?
2.參加 AWS Innovate 的報名費是多少?
3.誰適合參加 AWS Innovate?
4.我是否會收到 AWS Innovate 註冊確認?

問︰AWS Innovate 在何處舉辦?
答:AWS Innovate 是線上會議。完成線上註冊後,您會收到含有存取平台所需登入連結的確認電子郵件。您將能夠在 2020 年 2 月 19 日存取該連結。

問︰參加 AWS Innovate 的報名費是多少?
答:AWS Innovate 是免費的線上會議。

問︰誰適合參加 AWS Innovate?
答︰無論您是第一次使用 AWS 的新手或是有經驗的使用者,在 AWS Innovate 都會有新的收穫。AWS Innovate 旨在協助您開發合適的技能,以開拓新的洞見,提升效率並做出更準確的預測。

問︰我是否會收到 AWS Innovate 註冊確認?

答:如果您已完成觀看 5 次或以上的專題講座,我們會在活動結束後一週,將參加證明寄送到您用來註冊活動的電子郵件。