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 Oliver 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。

+10,000

的客戶選用 AWS 提供的機器學習功能

89%

的雲端深度學習專案在 AWS 上執行

85%

的雲端 TensorFlow 專案在 AWS 上執行

83%

的雲端 PyTorch 專案在 AWS 上進行

專題講座說明

  • 繁體中文-國語場次
  • 英語場次
  • 繁體中文-國語場次
    • AI與ML的產業應用
    • 建置、訓練、部署ML模型
    • Amazon 的創新
    • AI未來發展
    • AI與ML的產業應用
    • Level 100~200:【如何發想AI及ML機器學習應用?以製造業爲例】

      機器學習可以解決那些問題?以製造業的商業需求為基礎,從產品端追溯回生產端的機器學習切入點

    • 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】

      由AWS 資深資料科學家 許躍騰 博士主講,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
    • Accelerate your ML journey
    • AI/ML Fundamentals
    • Build, train and deploy ML models
    • AI Services & Applications
    • AI/ML Services and Devices
    • Innovation at Amazon
    • Create tomorrow with data and machine learning

      Whether it’s 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.

      Speaker: Glenn Gore, Chief Architect, AWS

    • Accelerate your ML journey
    • Accelerating machine learning: Your role in the journey

      Artificial intelligence and machine learning hold the promise of transforming industries, increasing efficiencies, and driving innovation. While many executives are prioritizing machine learning, going from idea to implementation can be a daunting process that can stop companies before they even get started. Executives have an important role to play in accelerating the machine learning journey. In this session, we talk about the common challenges of machine learning implementations and share how AWS customers have been successful in introducing machine learning to rapidly achieve success against their business goals. We also share how customers are working with AWS to align teams and drive internal excitement for machine learning, provide developers with the right technical education, and identify the appropriate use cases to start with to go from idea to production.

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

      Learn Machine Learning like an Amazonian

      Machine Learning (ML) and Artificial Intelligence (AI) are stealing headlines and sparking the imaginations of big organizations and the start-up community. Yet, one of the biggest barriers to adoption of ML is finding and cultivating trained talent. Building upon the curriculum used to train thousands of Amazon’s own developers, AWS Training and Certification is providing resources to help make your ML vision a reality.

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

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

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

      Speaker: Kapil Pendse, Solutions Architect, AWS

      An overview of Amazon SageMaker security

      Amazon SageMaker is a fully managed machine learning service that data scientists and developers use to build predictive and analytical models with their data. Organizations often need to use sensitive data to train models and generate predictions. The session focuses on Amazon SageMaker, a fully managed service supporting the entire machine learning lifecycle, and touches on supporting AWS services that enable you to secure your machine learning workloads in the AWS Cloud.

      Speaker: Michael Stringer, Solutions Architect, AWS

      Accelerate Machine Learning projects with AWS Marketplace

      Companies spend significant time developing, searching, and evaluating algorithms and models to solve business problems using machine learning. AWS Marketplace recently launched a machine learning category which has hundreds of algorithms and model packages, that can be deployed quickly onto Amazon SageMaker, a fully-managed service that provides the ability to build, train, and deploy machine learning models. In this tech talk, we will give an introduction to AWS Marketplace for Machine Learning, and how you can solve your business problems using 3rd party algorithms and model packages faster with less effort and lower cost. We will walk through sample Jupyter Notebooks that you can use to experiment with model packages and algorithms from AWS Marketplace.

      Speaker: Kanchan Waikar, Senior Partner Solutions Architect, AWS

      Engaging Audiences Using Machine Learning in Media

      With the advancement of machine learning applications, new business opportunities are rapidly emerging in media. In this session, you learn how the AWS Media2Cloud solution can save time and reduce costs through setting up a serverless end-to-end ingest workflow to move your video assets and associated metadata to the cloud. You gain insight into how to make those assets even more valuable by enabling searching and indexing on your video library, and how to use Amazon Transcribe and Amazon Translate to take your live-streaming workflows to the next level with expert instruction on how to enable automatically created multilanguage subtitles.

      Speaker: Christer Whitehorn, Lead Solutions Architect, AWS Media Services

      ML workflows with Kubernetes and Amazon SageMaker

      Until recently, data scientists have spent much time performing operational tasks, such as ensuring that frameworks, runtimes, and drivers for CPUs and GPUs work well together. In addition, data scientists needed to design and build end-to-end machine learning (ML) pipelines to orchestrate complex ML workflows for deploying ML models in production. With Amazon SageMaker, data scientists can now focus on creating the best possible models while enabling organizations to easily build and automate end-to-end ML pipelines. In this session, we dive deep into Amazon SageMaker and container technologies, and we discuss how easy it is to integrate tasks such as model training and deployment into Kubernetes and Kubeflow-based ML pipelines.

      Speaker: Arun Balaji, Partner Solutions Architect, AISPL

    • Build, train and deploy ML models
    • Automate machine learning life cycle and get your ML models to production faster

      Machine learning involves more than just training models; you need to source and prepare data, engineer features, select algorithms, train and tune models, and then deploy those models and monitor their performance in production. Learn how to automate business-critical machine learning workloads from start to finish. In this session, we cover how to help you identify and detect anomalies in your ML models, and radically reduce troubleshooting time in building and training high-quality ML models.

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

      Automatically build ML models with accurate training datasets and implement human review in ML predictions

      Successful machine learning models are built on the shoulders of large volumes of high-quality training data. Building machine learning (ML) models has traditionally required a binary choice. Amazon SageMaker Autopilot eliminates this choice, allowing you to automatically build machine learning models without compromises. In this session, learn how SageMaker Autopilot automatically explore different solutions to find the best model. Find out how to label datasets and directly deploy the model to production with just one click, or iterate on the recommended solutions to further improve the model quality. We conclude the session with how Amazon A2I makes it easy to build and manage human reviews, and allow predictions for machine learning applications.

      Speaker: Tapan Hoskeri, Solutions Architect, AISPL

      ML model deployment techniques using Amazon SageMaker Managed Deployment, Amazon Elastic Inference, Amazon Neo and AWS Inferentia

      Machine learning can be very resource intensive and you will not be able to deploy a machine learning model until it is trained. At AWS, we are constantly working to make training models efficient, faster and cheaper. However, model inference is where the value of machine learning is delivered. This is where speech is recognized, text is translated, object is recognized in a video, manufacturing defects are found, and cars get driven. This session analyzes the common pain points we face in running machine learning and deep learning inference workloads. It also explains how AWS is addressing these pain points as you add intelligence to your applications and scale these workloads.

      Speaker: Sujoy Roy, Senior Data Scientist, AWS

      Accelerate building of deep learning applications

      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. Learn how you can quickly launch Amazon EC2 instances pre-installed with popular deep learning frameworks and interfaces such as TensorFlow, PyTorch, Apache MXNet, Chainer, Gluon, Horovod, and Keras to train sophisticated, custom AI models, experiment with new algorithms, or to 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
    • Build high quality fraud detection ML models and detect fraud online rapidly

      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 different 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 protection of your company and customers.

      Speaker: Eric Greene, AI Specialist Solutions Architect, AWS

      Speed up and simplify time-series forecasting and real-time personalization to solve complex business challenges

      Deploying custom machine learning models to solve complex business challenges are hard, but it doesn't have to be. Based on the machine learning technology perfected from years of use on Amazon.com, Amazon Forecast and Amazon Personalize enable developers with no prior machine learning experience to easily build accurate forecasting and sophisticated personalization capabilities into their applications. Using AutoML, a new process that automates complex machine learning tasks, these services perform and accelerate the difficult work required to design, train, and deploy a machine learning model that is customized for your data. In this session, we show you how to use Amazon Personalize and Amazon Forecast to create individualized recommendations for customers and deliver highly accurate forecasts. Both run on fully managed infrastructure and provide easy-to-use recipes that deliver high-quality models even if you have little machine learning experience.

      Speaker: Anand Iyer, Enterprise Solutions Architect, AISPL

      Amazon Kendra: Reinventing enterprise search and interaction with data using AI

      How can you get the most accurate and specific answer to a search query when the answer may be hidden within various enterprise information systems? This session teaches you how to use Amazon Kendra, an enterprise search solution that will give you straightforward answers to questions like, “How much is the cash reward on the corporate credit card?” Learn how this can improve cross-team knowledge sharing, enhance sales, and customer support services, and make it much easier for end consumers to find the information they need.

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

      Breaking language barriers with AI

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

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

      Power cloud contact center with analytics using machine learning

      Today, most contact center analytics are based on phone switch activity or customer relationship management (CRM) call notes that are typed in by the contact center agent. What's typically missing from these analytics are insights into the actual conversations between agents and customers. In this session, we explain how Amazon Connect have been trained specifically to understand the nuances of contact center conversations including multiple languages and custom vocabularies. Learn how with Contact Lens for Amazon Connect, customer service supervisors can conduct fast, full-text search on call and chat transcripts to quickly troubleshoot customer issues; while leveraging call and chat-specific analytics, including sentiment analysis and silence detection to improve customer service agents’ performance.

      Speaker: Sumit Patel, Enterprise Solutions Architect, AWS

    • AI/ML Services and Devices
    • Train, evaluate, and tune your reinforcement learning model with AWS DeepRacer

      In this session, we introduce the basics of reinforcement learning, show you how to apply this knowledge to start training your own autonomous vehicle models, and test them in a virtual car racing experience powered by AWS DeepRacer. We show off the single-car time-trial format or dual-car head-to-head racing challenges in the AWS DeepRacer 3D racing simulator. After joining this session, you’ll have enough knowledge to begin competing in the AWS DeepRacer League, which provides an opportunity for you to compete for prizes and meet fellow machine learning enthusiasts, online and in-person.

      Speaker: Gabe Hollombe, Senior Developer Advocate, AWS

      Automate code reviews and application performance recommendations with Amazon CodeGuru

      Even for the most seasoned engineers, it can be difficult to detect some types of code issues and challenging to identify the most expensive lines of code without performance engineering expertise. Amazon CodeGuru is a new machine learning service that helps you catch code issues faster and improve application performance. In this session, you get the details and a demo on how CodeGuru works. CodeGuru reviews Java code in your GitHub and AWS CodeCommit source code repositories, and it profiles your applications and searches for optimizations even in production. It also provides intelligent recommendations so that you can action immediately to fix and improve code issues and inefficiencies.

      Speaker: Atanu Roy, Principal Solutions Architect, AISPL

      Customize image and video analysis with AI and machine learning

      Companies are using computer vision to understand the content and context of their images and videos at scale. This session provides an overview of the nuances to consider when developing your own application. We explain the multiple approaches to solve the computer-vision needs of your business and next steps to develop a model for your specific use case. We conclude the session with learning how to use AWS DeepLens and Amazon Rekognition to build an application.

      Speaker: Vijoy Menon, Solutions Architect, AISPL

      Get started with generative AI using AWS DeepComposer

      Generative AI is one of the most fascinating advancements in artificial intelligence technology, and until now, developers interested in growing skills in this area haven’t had an easy way to get started. In this session, you learn about generative AI and get hands-on with AWS DeepComposer, the world's first machine learning–enabled musical keyboard for developers, to create an original composition. You are also introduced to concepts that you can use in Amazon SageMaker to do even more with generative AI. Developers, make some noise!

      Speaker: Julian Bright, AI Specialist Solutions Architect, AWS

活動時間
  • 香港
  • 台灣
  • 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
簡介
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
中級
專題講座著重於提供最佳實務、服務功能詳細資訊與示範,並假設與會者具有該主題的入門知識。
等級 300
進階
專題講座深入探討所選主題。講演者假設聽眾對該主題有所了解,但可能有或可能沒有實作類似解決方案的直接經驗。
等級 400
專家
專題講座面向熟悉該主題、已經實作自己的解決方案,並且對於技術在多種服務、架構和實作中的運作方式感到滿意的與會者。

重量級講師

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。

常見問答集

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?
5.我如何取得參加證明?
6.是否有採用其他語言的專題講座?
7.我如何與會議主辦人聯絡?

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︰完成線上註冊流程後,您會收到確認電子郵件。

問:我如何取得參加證明?
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。

問︰是否有採用其他語言的專題講座?
答:我們提供中文(國語)、韓文、印尼文、葡萄牙文和西班牙文等語言的專題講座。

問︰我如何與會議主辦人聯絡?
答︰如果您在常見問答集中找不到問題的解答,請以電子郵件聯絡我們