The sessions below are categorized. You can click on the topic to view the detailed description of each session.

AI & Machine Learning
Hybrid Cloud
Big Data
Infrastructure as Code
Data Lake
What's New
  • An Overview of AI on the AWS Platform (Level 100)

    AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning.


    AWS 提供一系列的智能服務,包括雲原生的機器學習和深度學習技術,以滿足您不同的用例和需求。 對於希望將人工智慧 (AI) 服務加到其應用程式的開發人員,AWS 的 Amazon Lex 能夠進行自然語言處理 Natural Language Understanding (NLU) 和自動語音辨識 Automatic Speech Recognition(ASR)、Amazon Rekognition 能夠進行視像搜索和圖像識別,而 Amazon Polly 則備有文字轉語音 (TTS) 的功能。我們的 Amazon Machine Learning 是專為開發人員而設的機器學習配套。

    對於更深入的深度學習應用程式,AWS Deep Learning AMI 可讓您在任何規模的雲端運行深度學習。 啟動預先安裝了開放源碼深度學習引擎(Apache MXNet,TensorFlow,Caffe,Theano,Torch和Keras)的 AMI 實例,以訓練自定義、更複雜的 AI 模型、對新的算法進行實驗,並學習新的深度學習技能 – 這些都由基於 GPU 的實例的自動縮放集群支援。  


    Learning Objectives:

    • Learn about the breadth of AI services available on the AWS Cloud
    • Gain insight into practical use cases for Amazon Lex, Amazon Polly, and Amazon Rekognition
    • Understand why Amazon has selected MXNet as its deep learning framework of choice due its programmability, portability, and performance


    Sze Lok Chan, Startup Business Development Manager, AWS

    Sze Lok has been working with Amazon Web Services across Hong Kong and Taiwan with a focus on assisting startups who are AWS Activate startup members to leverage AWS to build the minimal viable products (MVP) and expand their businesses around the globe.

    An Overview of AI on the AWS Platform
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  • Building Smart Applications with Amazon Machine Learning (Level 300)

    In this presentation, learn how an end-to-end smart application can be built in the AWS cloud. We will demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We will then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We will walk you through the process flow and architecture, demonstrate outcomes, and then dive into the code for implementation. In this session, you will learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.


    這次的線上研討會將介紹如何在 AWS 雲端上,建構端到端的智能應用程式。 我們將示範如何使用 Amazon Machine Learning (Amazon ML) 創建機器學習模型,並且把它們部署到生產環境,以及取得即時預測結果。我們將引導您了解整個開發流程和架構,以及執行結果,然後再深入介紹智能應用程式的程式碼。透過本課程,您將學習如何使用 Amazon ML以及如何將 Amazon ML 跟您的應用程式整合,並利用雲端進行預測分析。


    Learning Objectives:

    • Learn about AWS services needed to build smart applications on AWS, e.g. Amazon Kinesis, AWS Lambda, Amazon Mechanical Turk, Amazon SNS
    • Learn how to deploy such implementation
    • Get the code on GitHub for you to use immediately


    Dickson Yue, Solutions Architect, AWS

    Dickson has been working for Amazon Web Services across Hong Kong and Taiwan with a strong focus on building resilient, scalable, secure and cost effective application architectures. He assisted key customers building platforms that align revenue model and scale with their business. In Dickson’s beliefs, continuous innovation is required to survive in all businesses. It is the reason he helped customer to adopt new technologies around IoT and big data solutions.  Prior to his AWS time, Dickson was responsible to lead a technology team in a digital marketing company assisting APAC customer to execute digital strategy.

    Building Smart Applications with Amazon Machine Learning
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  • Integrate Amazon Chatbot with Any Messaging Service (Level 300)

    Amazon Lex offers built-in integrations with Slack, Twilio, Marketo, Salesforce, Quickbooks, Microsoft Dynamics, Zendesk, and Hubspot. But, you can also integrate with any other application by combining the Lex API and AWS API Gateway to extend your chatbots into virtually any use case with minimal effort.
    This session will show you how. The design pattern shown will be interesting to folks who want to build a pre-processing layer in front of Lex or want to route messages to multiple specialized bots.


    Amazon Lex 提供與 Slack、Twilio、Marketo、Salesforce、Quickbooks、Microsoft Dynamics、Zendesk 和 Hubspot 的內置集成。除此之外,您還可以透過組合 Lex API 和 AWS API Gateway 將 Amazon Lex 與任何其他應用程式進行集成,毫不費力的將您的聊天機器人擴展到幾乎任何用例。

    這場線上研討會,我們將向您展示如何做到這點。   我們所演示的設計模式能還您學習到如何在 Lex 上構建預處理層或將信息送到到多個特定的聊天機器人。


    Learning Objectives:

    • Learn how to setup and configure AWS API Gateway
    • Learn how to leverage AWS Lambda as the compute layer in front of Amazon Lex
    • Learn about Multi-bot architecture


    Damon Deng, Solutions Architect, AWS

    AWS 解決方案架構師;擁有17年IT 領域的工作經驗,先後在IBM,RIM,Apple 等企業擔任工程師、架構師等職位;目前就職於 AWS,擔任解決方案架構師一職。喜歡編程,喜歡各種編程語言,尤其喜歡Lisp。喜歡新技術,喜歡各種技術挑戰,目前在集中精力學習分佈式計算環境下的機器學習算法。

    Building Smart Applications with Amazon Machine Learning
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  • What's New on AI and Machine Learning? (Level 200)

    What's New on AI and Machine Learning? (Level 200)

    Amazon has a long history in AI, from personalization and recommendation engines to robotics in fulfillment centers. In re:Invent 2018, AWS launched more than 10 new machine learning services to make machine learning easier, faster, and less expensive. Our customers can choose from pre-trained AI services for computer vision, language, recommendations, and forecasting; Amazon SageMaker to quickly build, train and deploy machine learning models at scale; or build custom models with support for all the popular open-source frameworks.

    Click here to know more about the latest launches at AWS re:Invent 2018!


    AWS 的最新人工智能及機器學習服務概覽 (Level 200)

    Amazon 在人工智能方面有著悠久的歷史,從個性化和推薦引擎,到履行中心的機器人技術。在 AWS re:Invent 2018中,AWS 推出了超過 10 項新的機器學習服務,令應用機器學習變得更容易、更快、更便宜。 我們的客戶現在可以選擇預先訓練的人工智能服務,應用於計算機視覺、語言、建議和預測。Amazon SageMaker 則可以大規模快速構建、訓練和部署機器學習模型,或者構建支持所有流行的開源框架的自定義模型。


    按此了解更多有關 AWS re:Invent 2018 的最新發布。


    Kenneth Wong
    Business Development Manager, Amazon Web Services

    Kenneth has been working for Amazon Web Services with a strong focus on serving as trusted advisor to work with enterprise customers define and validate their cloud strategy and assist customers to adopt AWS. He also helps customer build effective, scalable and reliable solutions with AWS services and provide guidelines for creating cloud solutions that deliver the most value to his customers. He believes by assisting customers to adopt innovative technologies can help them accelerate their digital transformation.


    Kwunhok Chan
    Solutions Architect, Amazon Web Services

    Kwunhok, Solution Architect at Amazon Web Services Hong Kong, is an expert in DevOps, Containers and Serverless technologies. He has helped many customers move from legacy application architectures to modern cloud-native architectures. Prior to AWS, Kwunhok led a development team in building global distributed messaging systems that serve millions of users.

    What's New on AI and Machine Learning?
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