Welcome to AWS Innovate Online Conference –
AI and Machine Learning Edition

 February 19, 2020

Welcome to AWS Innovate Online Conference – AI and Machine Learning Edition, designed to inspire and empower you to accelerate innovation, scale effortlessly, and unlock new possibilities. Get the very latest in AI and Machine Learning from Glenn Gore, Chief Architect, AWS; Oliver Klein, Head of Emerging Technologies, AWS; and Dean Samuels, Lead Architect, AWS during the keynotes.

Dive deep into any of the 20+ breakout sessions across six tracks delivered by AWS experts; explore key concepts, use cases, best practices, live demos, and live Q&A to learn how other organizations are using AI and Machine Learning and walk away with the ability to implement these projects for your organizations.

Learn how AWS customers use machine learning to improve the quality of healthcare, fight human trafficking, provide better customer service, and protect you from fraud. With the broadest and deepest set of machine learning and AI services, they are creating new insights, enabling new efficiencies, and making more accurate predictions. That's why more than 10,000 customers have chosen to use AWS for machine learning.

+10,000

customers choose AWS for machine learning

89%

of deep learning projects in the cloud run on AWS

85%

of TensorFlow projects in the cloud happen on AWS

83%

of PyTorch projects in the cloud happen on AWS

Activities

20+ Sessions

20+ Sessions

AI/ML Experiential Showcase

AI/ML Experiential Showcase

Live QA

Live Q&A

AWS DeepRacer Innovate Challenge

AWS DeepRacer Challenge

Conference Time
  • ASIA PACIFIC
  • EMEA
  • NORTH AMERICA
  • LATIN AMERICA
  • ASIA PACIFIC
    • Australia
    • New Zealand
    • Singapore/Malaysia/Philippines
    • Thailand/Vietnam
    • Indonesia
    • Pakistan
    • India
    • Sri Lanka
    • Korea
    • Hong Kong
    • Taiwan
    • Australia
    • TIMING 1
      10.00AM - 2.00PM (AEDT)
      TIMING 2
      3.30PM - 7.30PM (AEDT)
    • New Zealand
    • TIMING 1
      12.00PM - 4.00PM (NZDT)
      TIMING 2
      5.30PM - 9.30PM (NZDT)
    • Singapore/Malaysia/Philippines
    • TIMING 1
      7.00AM - 11.00AM (SGT)
      TIMING 2
      12.30PM - 4.30PM (SGT)
      TIMING 3
      6.00PM - 10.00PM (SGT)
    • Thailand/Vietnam
    • TIMING 1
      6.00AM - 10.00AM (ICT)
      TIMING 2
      11.30AM - 3.30PM (ICT)
      TIMING 3
      5.00PM - 9.00PM (ICT)
    • Indonesia
    • TIMING 1
      6.00AM - 10.00AM (WIB)
      TIMING 2
      11.30AM - 3.30PM (WIB)
      TIMING 3
      5.00PM - 9.00PM (WIB)
    • Pakistan
    • TIMING 1
      4.00AM - 8.00AM (PKT)
      TIMING 2
      9.30AM - 1.30PM (PKT)
      TIMING 3
      3.00PM - 7.00PM (PKT)
    • India
    • TIMING 1
      4.30AM - 8.30AM (IST)
      TIMING 2
      10.00AM - 2.00PM (IST)
      TIMING 3
      3.30PM - 7.30PM (IST)
    • Sri Lanka
    • TIMING 1
      4.30AM - 8.30AM (SLST)
      TIMING 2
      10.00AM - 2.00PM (SLST)
      TIMING 3
      3.30PM - 7.30PM (SLST)
    • Korea
    • TIMING 1
      8.00AM - 12.00PM (KST)
      TIMING 2
      1.30PM - 5.30PM (KST)
    • Hong Kong
    • TIMING 1
      7.00AM - 11.00AM (HKT)
      TIMING 2
      12.30PM - 4.30PM (HKT)
      TIMING 3
      6.00PM - 10.00PM (HKT)
    • Taiwan
    • TIMING 1
      7.00AM - 11.00AM (CST)
      TIMING 2
      12.30PM - 4.30PM (CST)
      TIMING 3
      6.00PM - 10.00PM (CST)
  • EMEA
    • GMT
    • CET
    • CAT/SAST
    • EEST
    • MSK
    • GST
    • IST
    • GMT
    • GREENWICH MEAN TIME
      10.00AM - 2.00PM (UTC/GMT +0)
    • CET
    • CENTRAL EUROPEAN TIME
      11.00AM - 3.00PM CET (UTC/GMT +1)
    • CAT/SAST
    • CENTRAL AFRICA TIME / SOUTH AFRICAN STANDARD TIME
      12.00PM - 4.00PM CAT/SAST (UTC/GMT +2)
    • EEST
    • EASTERN EUROPEAN TIME
      12.00PM - 4.00PM EET (UTC/GMT+2)
    • MSK
    • MOSCOW TIME
      1.00PM - 5.00PM MSK (UTC/GMT+2)
    • GST
    • GULF STANDARD TIME
      2.00PM - 6.00PM GST (UTC/GMT +4)
    • IST
    • ISRAEL STANDARD TIME
      12.00PM - 4.00PM IST (UTC/GMT +2)
  • NORTH AMERICA
    • North America
    • North America
    • PACIFIC TIME
      9.00AM - 1.00PM PST (UTC/GMT -8)
      MOUNTAIN TIME
      10.00AM - 2.00PM MST (UTC/GMT -7)
      CENTRAL TIME
      11.00AM - 3.00PM CST (UTC/GMT -6)
      EASTERN TIME
      12.00PM - 4.00PM EST (UTC/GMT -5)
  • LATIN AMERICA
    • Latin America
    • Latin America
    • ARGENTINA TIME
      2.00PM - 6.00PM ART (UTC/GMT -3)
      CENTRAL STANDARD TIME
      11.00AM - 3.00PM CST (UTC/GMT -6)
      Brasília Time
      2.00PM - 6.00PM BRT (UTC/GMT -3)

Agenda

  •  Sessions in English
  •  Sessions in Korean, Mandarin, Bahasa Indonesia, Spanish & Portuguese
  •  Sessions in English
  • Agenda
  •  Sessions in Korean, Mandarin, Bahasa Indonesia, Spanish & Portuguese
  • Agenda

Session Description

  • 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 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.

    Speaker: 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

    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

    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

  • 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 (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

    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

    AWS Marketplace for machine learning

    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

    Evolution of personalization and recommendation for video workflows  

    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 

    Building machine learning workflows with Kubernetes and Amazon SageMaker

    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

    Scale machine learning from zero to millions of users

    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

    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 

    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

    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

    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

    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

    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

    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 

    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

    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

    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

    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.

    Speaker: Atanu Roy, Principal Solutions Architect, AISPL

    Large scale image and video analysis with Amazon Rekognition 

    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.

    Speaker: Imran Kashif, Senior Solutions Architect, AWS

    Get started with generative AI using AWS DeepComposer

    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

Level 100
Introductory
Sessions are focused on providing an overview of AWS services and features, with the assumption that attendees are new to the topic.
Level 200
Intermediate
Sessions are focused on providing best practices, details of service features and demos with the assumption that attendees have introductory knowledge of the topics.
Level 300
Advanced
Sessions dive deeper into the selected topic. Presenters assume that the audience has some familiarity with the topic, but may or may not have direct experience implementing a similar solution.
Level 400
Expert
Sessions are for attendees who are deeply familiar with the topic, have implemented a solution on their own already, and are comfortable with how the technology works across multiple services, architectures, and implementations.

Featured Speakers

Glenn Gore
Glenn Gore, Chief Architect, AWS

As the chief architect for AWS, Glenn is responsible for creating architectural best practices and working with customers on how they use the cloud and innovation to transform their own business or disrupt new markets.

Glenn has held previous roles in AWS, most recently as head of architecture for Asia Pacific and EMEA, where he managed regional teams working in the two fastest-growing regions. Glenn is a hands-on technologist with more than 20 years of experience in the technology industry. Prior to joining AWS, Glenn was the CTO of WebCentral, where he worked on highly scalable web platforms and big-data systems for customers. He also held roles at OzEmail and UUNET, the world's largest network provider.

Olivier Klein
Olivier Klein, Head of Emerging Technologies, AWS

Olivier is a hands-on technologist with more than 10 years of experience in the industry and has been working for AWS across APAC and Europe to help customers build resilient, scalable, secure, and cost-effective applications and create innovative and data-driven business models. He advises on how emerging technologies in the artificial intelligence, machine learning, and IoT spaces can help create new products, make existing processes more efficient, provide overall business insights, and leverage new engagement channels for consumers. He also actively helps customers build platforms that align IT infrastructure and service spending with revenue models, effectively reducing waste and disrupting the product development process that had been executed in the decades prior to that.

Dean Samuels
Dean Samuels, Lead Architect, AWS

Dean comes from an IT infrastructure background and has extensive experience in infrastructure virtualization and automation. He has been with AWS for the past five years and has had the opportunity to work with businesses of all sizes and industries, primarily across Australia and New Zealand, but also across the wider APAC region. Dean is committed to helping customers design, implement, and optimize their application environments for the public cloud to allow them to become more innovative, agile, and secure. While Dean does have a strong IT infrastructure background covering compute, storage, network, and security, he is very focused on bringing IT operations and software development practices together in a more collaborative and integrated manner.

FAQS

1. Where is AWS Innovate hosted?
2. What is the price of attending AWS Innovate?
3. Who should attend AWS Innovate?
4. Can I get a confirmation of my AWS Innovate registration?
5. How do I get the certificate of attendance?
6. Are there sessions in other languages?
7. How can I contact the online conference organizers?

Q: Where is AWS Innovate hosted?
A: AWS Innovate is an online conference. After completing the online registration, you will receive a confirmation email containing the login link that you will need to access the platform. You will be able to access the link on 19 February, 2020.

Q: What is the price of attending AWS Innovate?
A: AWS Innovate is a free online conference.

Q: Who should attend AWS Innovate?
A: Whether you are new to AWS or an experienced user, you can learn something new at AWS Innovate. AWS Innovate is designed to help you develop the right skills to create new insights, enable new efficiencies, and making more accurate predictions.

Q: Can I get a confirmation of my AWS Innovate registration?
A: After completing the online registration process, you will receive a confirmation email.

Q: How do I get the certificate of attendance?
A: If you complete watching 5 or more sessions, we will send a certificate of attendance one week after the event has ended, to the email you used when registering for the event.

Q: Are there sessions in other languages?
A: We have sessions in Korean, Bahasa Indonesia, Mandarin, Portuguese, and Spanish.

Q: How can I contact the online conference organizers?
A: If you have questions that have not been answered in the FAQs above, please email us.

 


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