AWS Innovate AI/ML Edition
Accelerate innovation, scale effortlessly, and unlock new possibilities with machine learning on AWS.
 Wednesday, 24 February, 2021


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Don't miss: AWS Innovate - Modern Applications Edition on October 27 & 28


Welcome to AWS Innovate Online Conference - AI & Machine Learning Edition, a free virtual event designed to inspire and empower you to accelerate your AI/ML journey. Whether you are new to AI/ML or an advanced user, AWS Innovate has the right sessions for you to apply AI/ML to your organization and take your skills to the next level.

 Why attend?

Join us as we feature AWS latest announcements, technologies, and innovations in AI/ML. Dive deep into business use cases, architectural, and deployment best practices. 

 Who should attend?

Whether you are getting started with AI/ML, an advanced user, a business executive, or curious about AI/ML, we have a specific track for your level of experience and job role.


Get inspired and learn how you can use machine learning to drive better experiences, streamline operations, and reduce risks, and walk away with the ability to implement these projects for your organization. Dive deep into any of the 50+ business and technical sessions led by AWS experts as they share the latest innovations in AI/ML, key concepts, business use cases, architectural best practices, and answer your questions live.

 Download Agenda at a Glance »

Select a Track:

  • Keynote
  • AI/ML: Solving the big issues
  • Accelerate AI/ML journey
  • Use cases of AI services
  • Prepare, build, train, deploy ML models
  • AI/ML fundamentals
  • AI/ML for Startups
  • Integration with ML
  • AWS DeepRacer
  • Hands-on labs
  • Builders Zone
  • Closing Remarks
  •  Korean
  •  Japanese
  •  Bahasa Indonesia
  • Keynote
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    Building a smarter, faster business using AI/ML on AWS (Level 100)

    In 2021, businesses across all industries will have opportunities to build deeper relationships with their customers, run more efficient operations, and pivot with new innovations than any year before. A major enabler of these is the accessibility of AI/ML for all types of organizations. Over the last year, AWS released several new services and features to help organizations launch more quickly, lower the technical bar to get started with AI/ML, and to reduce friction for hybrid and diverse platforms.

    Craig Stires, Head of AI and Machine Learning, APJ, AWS
    Olivier Klein, Lead Technologist, APJ, AWS

    Duration: 50mins

  • AI/ML: Solving the big issues
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    AI/ML: Solving the big issues

    About the track

    Hear from executives across various industries as they share what they have learnt and experimented with over the past months using agile technology, upskilling employees, and driving a culture of innovation to build for the future. You can also get insights into how uses AI/ML to personalize, scale, and invent new and compelling customer experiences.

    Innovation is never normal (Level 100)
    In 2020, the phrase ‘never normal’ became common language. And like most periods of major upheaval, the first instinct of some leaders is to focus on survival. For businesses working with AI and ML, however, living this never normal is simply ‘business as usual’, where constant change offers abundant opportunities to innovate and thrive.

    Join Olivier Klein, Lead Technologist, APJ, AWS as he presents the following customer stories.

    Customer story #1: Bowery Farming – The future of food production
    In the face of increasingly challenged global food supply chains, and the need to find more sustainable food production practices, Bowery Farming has turned to technology innovation to increase annual crop yields by a hundred times, using a fraction of the resources needed in traditional farming.
    Speaker: Henry Sztul, Executive Vice President of Science and Technology, Bowery Farming

    Customer story #2: Soul Machines – The digital humans enhancing customer experience
    Soul Machines brings the ‘human touch’ to transform customer and brand experiences, where machines manage repetitive and simple tasks so that real humans can manage the complex ones. Using a patented ‘Digital Brain’ its digital people contextualize customer interactions, adapting in real time in a similar way to actual human beings.
    Speaker: Greg Cross, Founder and CBO, Soul Machines

    Customer story #3: Transfix – Freight logistics transformed in a digital marketplace
    Leading tech start-up, Transfix, is hauling the $800 Billion US trucking industry into the 21st century to better match and connect shippers with carriers. Its AI and ML-based, digital freight marketplace ensures fairer pricing, increased trust and reliable service level agreements.
    Lily Shen, President and Chief Operating Officer
    Jonathan Salama, Co-founder and Chief Technology Officer, Transfix

    Customer story #4: University of Sydney – Protecting endangered species with AI and ML
    Preserving species diversity is vital to the future health of the planet and Australia is at the forefront of this challenge, with endangered flora and fauna species numbering in the thousands. Dr Carolyn Hogg and the team at the Australasian Wildlife Genomics Group, University of Sydney, use data science to accelerate the sequencing of Genomic data to save time, maximize conservation dollars and save beloved animals like the Tasmanian Devil.
    Speaker: Dr. Carolyn Hogg, Australasian Wildlife Genomics Group, University of Sydney

    To conclude the session, join Pradeep K. Dubey, Intel Senior Fellow and Director of the Parallel Computing Lab, and Olivier Klein, Lead Technologist, APJ, AWS, as they discuss the technology advances that have allowed AI to move from simple number crunching to making decisions. The ability of AI to help better predict future long tail, or Black Swan events such as COVID-19 is also explored.
    Speaker: Pradeep K. Dubey, Intel Senior Fellow and Director of the Parallel Computing Lab


    Driving innovation at Amazon (Level 100)
    At Amazon, everyone wants to innovate fast for customers. Many of our best ideas come ground up, from the people closest to customers. This session takes you behind-the-scenes to see how Amazon uses AI/ML to personalize, scale and invent new and compelling customer experiences. Focusing on insights gained and lessons learned, the session will cover the cultural, process, and technology aspects of building and scaling AI and Machine Learning capabilities across the organization.

    Part #1: enhancing CX
    From its inception,'s consumer retail business has transformed the shopping experience, from product search through to customer delivery. In this session, we share specific examples from's consumer retail businesses to demonstrate how AI & ML helps Amazon deliver the optimum customer experience, improves efficiency, and lowers the cost to serve.
    Speaker: Choong Lee, Strategic Business Development Manager, AWS

    Part #2: From contact center operator to brand ambassador
    Most of us can relate to the frustration a caller experiences with disproportionately long phone cues, and the need to repeat personal details time and again when seeking customer support to resolve a product or service issue. For customer services operators, the experience can be equally challenging. This session focuses on why decided to build Amazon Connect to serve millions of people daily and ensure personalized, dynamic, and positive customer experiences for all.
    Scott Brown, Head of Worldwide GTM, Productivity Applications, AWS
    Yasser El-Haggan, Head of Worldwide Solutions Architecture, Productivity Applications, AWS

    Part #3: Amazon Connect - From ‘Lift ‘n Shift’ to Customer Experience (CX) innovation
    More than a simple contact center technology stack, Amazon Connect offers a range of features that move customer service from reactive to proactive through innovations such as text to speech and highly contextualized customer data analysis to help all businesses create positive and compelling customer experiences.

    Scott Brown, Head of Worldwide GTM, Productivity Applications, AWS
    Yasser El-Haggan, Head of Worldwide Solutions Architecture, Productivity Applications, AWS


  • Accelerate AI/ML journey
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    Accelerate AI/ML journey

    About the track

    Provides customers with insights on how AI & ML inspires business innovation, transforms customer experiences and improves business outcomes. Whether it’s enhancing customer experiences, creating advanced real-time recommendations, accelerating new product development, boosting employee productivity to cutting costs and reducing fraud, organizations today are using AI and ML to solve business challenges and innovate faster.

    Strategies to accelerate AI/ML at scale: From idea to POC and achieve business outcomes (Level 100)
    AI and ML hold the promise of transforming industries, increasing efficiencies, and driving innovation. The key to machine learning success is scale. In this session, we cover how executives and managers who are looking to achieve success using ML at scale get guidance including mechanisms to build an effective system to accelerate innovation and drive technological progress. We also share best practices in implementing MLOps and data governance to overcome ML implementation challenges. We explain how customers who have been successful are working with us to align teams in introducing ML, driving ML excitement, and providing developers within their organization the right technical education to achieve business outcomes.

    Bernard Leong, Head of Machine Learning and Artificial Intelligence, ASEAN, AWS
    Chris Howard, Head of AI/ML Solutions Architecture, APJ, AWS
    Duration: 60mins

  • Use cases of AI services
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    Use cases of AI services

    About the track

    Learn how AWS AI services are applied to applications and used in real-life use cases. We focus on how AI services can easily integrate with applications to address common use cases such as personalized recommendations, modernizing contact center, improving safety and security, and increasing customer engagement with no machine learning skills required. 

    Building intelligence into the contact center (Level 200)
    Your contact centre is the biggest touchpoint between you and your customers, and every engagement can provide your team with powerful insights. In this session, we show you how to leverage the new capabilities in Amazon Connect such as Contact Lens to transcribe calls, do sentiment analysis, and surface valuable customer insights from every conversation powered with machine learning. Learn how to discover emerging themes and trends from customer conversations in real-time, which allow you to respond faster and serve your customers better. We also cover the new features that were recently announced for Amazon Connect and walk you through how you can use them to improve your customer interactions and experiences.

    Speaker: Sumit Patel, Enterprise Architect, AWS
    Duration: 30mins

    Intelligent document processing: Building higher accuracy document automation at scale (Level 200)
    Organizations have millions of physical documents and forms that hold critical business data. These documents, such as insurance claims or loan applications, have structured and unstructured data that are either extracted by humans or by rule-based systems which are not easily scalable, have high costs, and could produce low-accuracy extraction results. In this session, learn how to use Amazon Textract, Amazon Comprehend, and Amazon Augmented A2I to extract structured data, redact sensitive information, and deploy your automated document processing workflow into production, at scale. 

    Speaker: Jonathan Hedley, Principal AI/ML Specialist Solutions Architect, AWS
    Duration: 30mins

    Improve customer engagement and conversion with personalized user experiences (Level 200)
    As the ability to deliver more sophisticated digital experiences evolve over time, the expectation and demand from customers to receive a more personalized experience from companies and products they engage with have also increased. Customers today expect real-time, curated experiences across digital channels as they consider, purchase, and use products and services. In this session, we deep-dive into using Amazon Personalize to create and manage personalized recommendations efficiently, letting you focus on the real value of the data for your business. Learn how to build applications capable of delivering a wide array of personalized experiences, including specific product recommendations, personalized product re-ranking, and customized direct marketing - with no ML experience required. 

    Speaker: Alex Thewsey, AI/ML Specialist Solutions Architect, AWS 
    Duration: 30mins

    Create smarter bots with intelligent search and improve customer satisfaction (Level 200)
    Customer service conversations typically revolve around one or more topics and contain related questions. Answering these questions seamlessly is essential for a good conversational experience. In this session, learn how you can build an intelligent bot with Amazon Lex and integrate it with Amazon Kendra. Amazon Kendra provides you with a highly accurate and easy-to-use enterprise search service powered by machine learning. It offers a more intuitive way to search—using natural language—and returns more accurate answers. You simply point Amazon Kendra at your content, and Amazon Kendra indexes the content to provide the answers. With this solution, customers get a response right away, and support staff can focus on solving problems and improving customer satisfaction.

    Speaker: Sara van de Moosdijk, ML Specialist Solutions Architect, AWS
    Duration: 30mins

    Detect online fraud in real-time and at scale (Level 200)
    For more than two decades, Amazon has been fighting fraud across all its online businesses, from the merchant side, with and subsidiary businesses like Zappos, to AWS digital services. This breadth of experience fighting online fraud includes payments fraud, fake accounts, fake reviews, promotion abuse, and account takeovers. Amazon Fraud Detector is a fully managed AI service that uses machine learning (ML) and more than 20 years of fraud detection expertise from Amazon to identify potentially fraudulent activities so that customers can catch more online fraud faster. Learn how Amazon fraud mitigation strategies influenced the development of Amazon Fraud Detector, and hear how organizations are applying it to prevent and detect online fraud, and deliver successful outcomes.

    Eric Greene, Lead AI and Machine Learning Solutions Architect, Public Sector, APJ, AWS
    Tom Liu, Technical Account Manager, ANZ, AWS
    Duration: 30mins

    Using AI to streamline media content operations (Level 200) 
    Reviewing, searching, and analyzing image and video content at scale remains a top challenge for media and entertainment organizations. Learn how organizations are using Amazon Rekognition and Amazon Rekognition Custom Labels to get more out of their content archives.

    Speaker: Kashif Imran, Principal Solutions Architect - Amazon AI, AWS
    Duration: 30mins

  • Prepare, build, train, deploy ML models
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    Prepare, build, train, deploy ML models

    About the track

    Learn how to easily build custom trained ML models with existing algorithms or pre-trained models. Understand best practices to decide what, where, and how when putting ML solutions into production, illustrate on what the model is and what the business context is, as well as where to deploy and how to deploy. This track also includes working backwards from customer questions, and implementing and scaling ML models with MLOps.

    Jumpstart to prepare, build, train, and deploy ML models on AWS (Level 200)
    Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. In this session, we provide an overview for one of the fastest growing services in AWS history. Amazon SageMaker is built on Amazon’s two decades of experience developing real-world machine learning applications, including product recommendations, personalization, intelligent shopping, robotics, and voice-assisted devices. Learn how to prepare, build, train, tune, deploy, and manage your first machine learning model on AWS.

    Speaker: Tapan Hoskeri, Solutions Architect, AISPL
    Duration: 30mins

    Build and manage training datasets for machine learning (Level 200)
    Preparing training data is a critical step in machine learning. Preparing data involves creating labelled data, creating features, visualizing the features, and processing the data so it can be made available for training. In this session, learn how to use SageMaker Data Wrangler to connect to the data sources, use prebuilt visualization templates and built-in data to transform and streamline the process of cleaning, verifying, and exploring data without having to write a single line of code. In this session, we provide a demonstration of how SageMaker Data Wrangler publishes the data to SageMaker Feature store and explain how to take data preparation workflows into production using SageMaker Pipelines. 

    Speaker: Praveen Jayakumar, Principal Solutions Architect, AI/ML, AISPL
    Duration: 30mins

    Incorporating explainability and fairness-awareness in ML solutions (Level 300)
    Machine learned models and data-driven systems are being increasingly used to help make decisions in application domains such as financial services, healthcare, education, and human resources. With the goal that a significant portion of these decision systems becoming fully-automated, there is need for understanding and rectifying the underlying bias in data, algorithms, and objectives, including providing reliable explanations for the predictions and decisions taken by these machine learning (ML) systems. In this session, we share how Amazon SageMaker Clarify address some of the regulatory, business and data science questions that arise in the context of explainability and fairness in ML. We would also specifically dive deep into how builders can incorporate these best practices in explainable and fairness-aware ML into their solutions using these AWS services.

    Speaker: Sujoy Roy, Senior Data Scientist, AWS
    Duration: 30mins

    Build enterprise scale ML workflows on Kubernetes and Amazon SageMaker with Kubeflow (Level 200)
    Until recently, data scientists had to spend significant time performing operational tasks, such as ensuring frameworks, runtimes, and drivers for CPUs and GPUs are working well together. They are 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. Kubeflow Pipelines is an add-on to Kubeflow that allows you to build and deploy portable and scalable end-to-end ML workflows. In this session, learn how you can integrate Amazon SageMaker features such as data labeling, large-scale hyperparameter tuning, distributed training jobs, and secure scalable model deployment using SageMaker Components for Kubeflow Pipelines.

    Speaker: KJ Pittl, ISV Solutions Architect, AWS
    Duration: 30mins

    Fully-managed ML deployments on AWS (Level 200)
    AWS offers and delivers the broadest choice of powerful compute, high speed networking, and scalable high-performance storage options for any machine learning (ML) project or application. You can also choose the ML infrastructure to implement a fully managed ML Deployment approach with Amazon SageMaker. In this session, we explore how to deploy your inference models on AWS, what factors to consider and how to optimize the deployments. We share best practices and approaches to get your ML workloads running smoothly and efficiently on AWS.

    Speaker: Eshaan Anand, Senior Partner Solutions Architect, AWS
    Duration: 30mins

    Accelerating machine learning innovation securely with Amazon SageMaker (Level 200)
    To build successful machine learning models you need datasets unique to your organization. These datasets are extremely valuable assets and need to be secured throughout every step of the machine learning process. In a typical machine learning project it can take months to build a secure workflow before you can begin any work on your models. Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly and securely. In this session, we provide an overview of the Amazon SageMaker security features that help organization meet the strict security requirements of machine learning workloads.

    Speaker: Michael Stringer, Senior Solutions Architect, AWS
    Duration: 30mins

  • AI/ML fundamentals
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    AI/ML fundamentals

    About the track

    This track features new AI/ML announcements that will excite the developers. We share how to design and build machine learning (ML) pipelines to orchestrate complex workflows when deploying ML models. 

    How to create multi-account ML workflows with Amazon SageMaker Pipelines (Level 300)
    Machine learning workflows are hard to build because you need to create hundreds of code packages for data preparation, model training and deployment, and stitch them together so they run as a sequence of steps. In this session, learn about Amazon SageMaker Pipelines, the world’s first machine learning CI/CD service designed to be accessible for every developer and data scientist. Amazon SageMaker Pipelines brings CI/CD pipelines to machine learning, reducing the months of coding previously required to just a few hours. We show how you can create a multi-account deployment pipeline using a custom SageMaker project template and discover how Amazon SageMaker Pipelines manages dependencies and orchestrates the workflow. 

    Speaker: Julian Bright, Senior AI/ML Specialist Solutions Architect, AWS
    Duration: 30mins

    Automate code reviews, performance recommendations, and operational insights (Level 300)
    A better understanding of your code base helps reduce overall costs, improves non-functional behaviors like application response times and performance, and allows you to tackle issues faster and more accurately. Similarly, from the operational front, it can be difficult to identify operational issues long before they impact your customers. In this session, learn more about Amazon CodeGuru, a developer tool for automating code reviews to detects issues such as deadlocks, data races on thread unsafe classes, atomicity violations and over-synchronization related to concurrency bugs. The session also includes automating performance reviews through application profiling, identify lines of expensive object recreation, usage of inefficient libraries, logging and concurrency issues that improves code performance for applications in production. In addition, the session covers Amazon DevOps Guru which makes it easier for developers and operators to automatically detect operational issues and recommend options for remediation or mitigation that improves overall applications availability, operational performance and insights while reducing expensive downtime.

    Speaker: Aashmeet Kalra, Senior Solutions Architect, AWS
    Duration: 30mins

    Choosing the right ML algorithms for the different use cases (Level 300)
    AWS offers many choices for solving business problems through machine learning (ML), ranging from built-in algorithms to frameworks and more in using ML services. Amazon SageMaker supports the different built-in ML algorithms, such as classification, regression, and recommendation. Built-in algorithms are easy to use, and they are optimized for speed, scale, and accuracy. In this session, learn how to choose the right built-in algorithm for your business problem. This session categorizes these algorithms by problem types and dives deep into popular ones. Bring your curiosity and walk away with the information you need to choose the right built-in algorithm for your business requirements.

    Speaker: Pedro Paez, Senior AI/ML Specialist Solutions Architect, AWS
    Duration: 30mins

    Accelerate machine learning projects with pretrained models (Level 200)
    Insufficient enterprise AI adoption often happens due to lack of time, data, and skills required to develop ML models for solving your business problems. In this session, learn how pretrained ML models which is available in AWS Marketplace and deployed with Amazon SageMaker can help you quickly add new ML features in your applications and enable you to prove the value of powering your applications with AI to your leadership. Learn how to explore, test, deploy, and integrate ML models securely in your existing production application.

    Speaker: Kanchan Waikar, Senior Partner Solutions Architect, AWS Marketplace
    Duration: 30mins

  • AI/ML for Startups
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    AI/ML for Startup

    About the track

    Learn how Startups can easily leverage AWS AI/ML stack to quickly and painlessly build their Startups. Understand best practices, new product launches and hacks to build a cost effective and scalable ML solution on AWS. Learn from experienced founders how you can easily avoid common pitfalls.

    Build AI-powered applications without any machine learning expertise (Level 100)
    Don’t let the idea of integrating artificial intelligence (AI) or machine learning (ML) into your workflow intimidate you. Whether you’re trying to improve your customer support workflow or automatically review your code for security vulnerabilities, there are ML solutions available for startups regardless of their technical skills. We show you ready-made workflows that you can implement to improve your customer support, review your code for security vulnerabilities, help improve business outcomes, and how to raise your next fundraising round.
    Speaker: Allie K. Miller, Global Head of Machine Learning BD, Startups & VC, AWS
    Duration: 30mins

    Scaling your startup: What to expect when you’re building an ML Team (Level 100)
    Scaling up from a single engineer working off of their laptop to a dedicated team is an exciting milestone. But with growth comes growing pains. As you scale up your machine learning (ML) team, it is essential to leverage cloud services and tools just like you do for the rest of your development teams. Discover how to set up a data lake and implement it into a machine learning experiment workflow. Learn how to prepare an end-to-end workflow to easily share the workload, and other tips for scaling your startup
    Speaker: Rob Ferguson, Principal Business Development Manager AI/ML, Startups & VC, AWS
    Duration: 30mins

    Confessions of AI/ML Startup founders (Level 100)
    Hear from Startups founders as they share how they use AWS AI/ML services to unlock new possibilities and deliver business outcomes at scale. At the same time learn from these experienced founders on how you can easily avoid common pitfalls for your Startup. 

    Speaker: Andrew Vranjes, Head, Startup Business Development, ASEAN, AWS
    Duration: 30mins

  • Integration with ML
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    Integration with ML

    About the track

    Learn about the various ML integrations available that can help you build, train, and deploy your ML models efficiently and at scale.

    MLOps for edge devices with Amazon SageMaker Edge Manager (Level 200)
    In this session, learn more about Amazon SageMaker Edge Manager, a new capability of Amazon SageMaker that helps developers operate machine learning (ML) models on a fleet of edge devices and solve challenges with constraints and maintenance of ML models on edge devices. Find out how you can use Amazon SageMaker Edge Manager to build an MLOps pipeline from the cloud to the edge and back by preparing multiple variants of a model, including deployment on different edge devices, monitoring models deployed across a fleet, capturing data samples from each model instance on each device, and sending data securely from the fleet to the cloud for labeling and retraining with SageMaker.

    Speaker: Kapil Pendse, Senior Solutions Architect, AWS
    Duration: 30mins

    Accelerate innovation and ML workloads using your data with Amazon FSx for Lustre and Amazon S3 (Level 200)
    Organizations have accumulated massive amounts of data, and are continuing to accumulate data. With all that stored data, how can customers easily leverage the value of their data to accelerate machine learning, analytics or HPC. In this session we focus on showing how you can seamlessly leverage Amazon FSx for Lustre and Amazon S3, whether it’s with a compute fleet or Amazon SageMaker, to supercharge your workloads, to accelerate business outcomes.

    Speaker: Wali Akbari, Senior APJ Storage Solutions Architect, AWS
    Duration: 30mins

    Create, train, and deploy machine learning (ML) models using familiar SQL commands (Level 200)
    Using data in your data warehouse for machine learning use cases like churn prediction can be complicated because of the different tools and skills required. In this session, learn how with Amazon Redshift Machine Learning, you can use SQL to automatically create, train, and apply machine learning (ML) models with the data in your data warehouse using familiar SQL commands. Join this session to learn how to leverage this new Amazon SageMaker integration to embed predictions like fraud detection and risk scoring directly in queries and reports, without any prior ML experience. 

    Speaker: Suman Debnath, Principal Developer Advocate, AISPL 
    Duration: 30mins

  • AWS DeepRacer
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    AWS DeepRacer

    About the track

    Compete for prizes and meet fellow machine learning enthusiasts, online. Racers will have the opportunity to join the DeepRacer online session and have a 1-to-1 chat with our machine-learning experts.

    Get rolling with machine learning on AWS DeepRacer (Level 200)
    Developers, start your engines! This session provides developers of all skill levels an opportunity to get hands-on experience with AWS DeepRacer and hear about exciting announcements and enhancements coming to the league in 2021. Learn about the basics of machine learning and reinforcement learning (a machine learning technique ideal for autonomous driving). In this session, you can build a reinforcement learning model and submit it to the AWS DeepRacer League for a chance to win prizes and glory.

    Speaker: Janos Schwellach, Specialist SA Developer, AWS
    Duration: 30mins

    Shift your ML model into overdrive with AWS DeepRacer analysis tools (Level 300)
    Make your way from the middle of the pack to the top of the AWS DeepRacer podium. Once you have built your first reinforcement learning model, extend your machine learning skills in this session by exploring how human analysis of reinforcement learning through logs improves your performance through trend identification and optimization to better prepare for the races. 

    Speaker: Donnie Prakoso, Senior Developer Advocate, AWS
    Duration: 30mins

  • Hands-on labs
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    Hands-on labs

    About the track

    Learn from a series of hands-on labs, and chat with our AWS experts and understand how to get started, get certified and build your own learning path moving forward.

    Personalized recommendations (Level 200)
    In this lab, learn the basics of how to use Amazon Personalize in order to create a recommendation system. Amazon Personalize is a service which is based off the same technology used at It is designed for users who would like to have a managed recommendation engine, but may not have the experience required to build their own.

    Virtual contact center (Level 200)
    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. Explore a set of capabilities for Amazon Connect enabled by machine learning (ML) that gives contact center supervisors and analysts the ability to understand the content, sentiment, and trends of their customer conversations.

    Enterprise search with Amazon Kendra (Level 200)
    In this lab, we demonstrate using Amazon Kendra to setup our own Enterprise Search instance, index HTML/PDF content in Amazon S3, and use a variety of query types to return accurate search results for end users.

    Customer churn prediction (Level 200)
    In this lab, learn how identifying unhappy customers early provides you with the opportunity to incentivize them to stay and helps decrease customer churn. 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.

    Automating code review with Amazon CodeGuru Reviewer (Level 200)
    In this lab, we walk you through how to associate Amazon CodeGuru with your repo and automate code review using Amazon CodeGuru Reviewer.

    Build, train, and debug machine learning models (Level 200)
    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.

    Text extraction and analysis with Amazon Textract and Amazon Comprehend (Level 200)
    In this lab, we extract the features of a text document using Amazon Textract then use Amazon Comprehend to analyze the extracted features. Learn how to connect AWS services together using AWS Lambda.

    Sentiment analysis web app (Level 200)
    In this lab, we demonstrate how to add AI and ML cloud services features to your web application with React and the Amplify Framework.

    AWS DeepRacer (Level 200)
    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.

  • Builders Zone
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    Builders Zone

    About the track

    Dive deep into technical stacks, learn how AWS experts have helped solve real-world problems for customers, try out these demos with step-by-step guides, and walk away with the ability to implement these or similar solutions in your own organization.

    Worker safety system using customized image and video analysis (Level 300)
    Learn how 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 session, 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: Kashif Imran, Principal Solutions Architect - Amazon AI, AWS

    Real-time voice translator (Level 200)
    Whether you are travelling abroad, attending a webinar or a conference in a different language, one faces challenges in communication. In this session, learn how to break this language barrier by creating android web based real-time voice translator application by using some of the very simplistic and powerful AWS AI services, and learn how to power your application with real-time voice translation.

    Speaker: Darshit Vora, Startup Solutions Architect, AISPL

    Multilingual omnichannel contact center (Level 200)
    Language barriers between a customer and agent can be a challenge for any contact centre. In this session, we show how to automatically translate chat conversations between users in real-time, in the preferred language across different channels, allowing for a better customer experience.

    Gaurav Sahi, Principal Solutions Architect, AISPL
    Jackysh Bangera, Solutions Architect, AISPL

    Workplace safety with Amazon Rekognition (Level 300)
    Today, businesses are looking for ways to adapt to the challenges created by COVID-19. For example, retailers need to keep their employees and customers safe as they interact in physical proximity. Join this session to learn how you can use Amazon Rekognition, a fully managed computer vision service to build automated and scalable workplace safety compliance solutions.

    Speaker: Kashif Imran, Principal Solutions Architect - Amazon AI, AWS

    Build an AI-enabled workflow for language agnostic feedback management system (Level 200)
    With more than 4.6 billion internet users today, the dependency on online feedback and reviews before making any purchase decision is increasing daily. This makes addressing those feedback efficiently an important aspect for every B2C organization. In this demo, we show how to build a human independent workflow for feedback management system. Learn how to use Amazon Translate, a neural machine translation service that delivers fast, high-quality, and affordable language translation and natural language processing service and Amazon Comprehend to find insights from customer feedback. We also cover how to leverage AWS services including Amazon Translate, Amazon DynamoDB and AWS Lambda, as well as share the event- driven serverless workflow with AWS Step Functions to develop the feedback management system.

    Speaker: Nitin Dhir, Startup Solutions Architect, AISPL

    Garbage underwater detection (Level 300)
    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

    Sign and Speak (Level 300)
    This demo showcases how the Sign & Speak program uses machine learning (ML) to build a tool that facilitates communication between users of sign language and users of spoken language. By combining artificial intelligence (AI) models trained to transcribe speech and interpret sign language with a camera and a microphone, the tool enables two-way conversation in situations where communication was previously challenging.

    Speaker: Eshaan Anand, Senior Partner Solutions Architect, AWS

    Build an intelligent marketing kiosk (Level 200)
    In this demo, we show how to build a smart ad display to serve relevant advertisements in real-time, based on the inference from the audience looking at the ads. Advertising displays serve static or periodically shuffling ads, which change at regular intervals usually geared towards one segment of buyers. This results in a missed opportunity in terms of catering to other segments who would be near a billboard or a display. Learn how to build an intelligent solution where an advertising display uses an on-device camera, or feeds from nearby CCTV cameras of people passing by, to identify the audience and serving them personalized advertisements in near real time. We also cover how Amazon SageMaker and Amazon Rekognition can extract attributes like age, gender, height, face-positioning and use these attributes with Amazon Personalize to serve more relevant and targeted advertisements.

    Speaker: Vatsal Shah, Solutions Architect, AISPL

    Build a self-service know your customer application (Level 200)
    Join us in this demo to discover how simple it is to build a data-driven web application to automate manual and time-consuming processes. Learn how Know Your Customer (KYC) application conducts liveness detection check by requesting the user to perform random actions and validate these actions with Amazon Rekognition. We share how the user can upload key identification documents, leverage on Amazon Rekognition and Amazon Comprehend to analyze the content and gather name, date of birth and other key information. We also cover how this application can easily compare snapshots of the user's face and with the photo identification provided.

    Speaker: Arun Kumar Lokanatha, Startup Solutions Architect, AISPL

    Identity verification with Amazon Rekognition (Level 300)
    In today’s economy, businesses are looking to enable digital identity verification by creating scalable authentication workflows. For example, financial businesses need to verify customer identity before accessing online services, and educators need to verify student identity during remote tests. In this session, learn how to use Amazon Rekognition, a fully managed computer vision service to build automated and scalable online identity verification solution.

    Speaker: Kashif Imran, Principal Solutions Architect - Amazon AI, AWS

  • Closing Remarks
  • closing-remarks

    Closing Remarks

    Freedom to reinvent with AI/ML

    2020 was a year unlike any other. Companies and businesses of all sizes and governments new and old had to change across all facets, and technology helped manage these changes. Rather than slow us down, 2020 accelerated our shift to a digital world, and I anticipate we won’t go back any time soon. This session provides a recap of the days' sessions and addresses some of the commonly asked questions related to AI/ML with AWS. Additionally, learn how AWS is freeing builders to innovate on machine learning with the latest developments in AWS machine learning, demos of new technology, and insights from customers. To conclude, the session focuses on how 2021 will be a launchpad for all kinds of change to continue that acceleration.

    Speaker: Dean Samuels, Lead Architect, ASEAN, AWS

  •  Korean
  •  Japanese
  •  Bahasa Indonesia

Conference Time

  • Australia & New Zealand
  • ASEAN & Pakistan
  • India & Sri Lanka
  • Korea
  • Japan
  • Australia & New Zealand
  • Australia
     GMT+11 (AEDT)

    Timing: 11.00am - 4.00pm

    New Zealand
     GMT+13 (NZDT)

    Timing: 1.00pm - 6.00pm

  • ASEAN & Pakistan
  • Singapore
     GMT+8 (SGT/MYT/PHT)

    Timing 1: 8.00am - 1.00pm
    Timing 2: 1.30pm - 6.30pm

     GMT+7 (ICT)

    Timing 1: 7.00am - 12.00pm
    Timing 2: 12.30pm - 5.30pm

     GMT+7 (WIB)

    Timing 1: 0700 - 1200
    Timing 2: 1230 - 1730

     GMT+5 (PKT)

    Timing 1: 5.00am - 10.00am
    Timing 2: 10.30am - 3.30pm

  • India & Sri Lanka
  • India
     GMT+5.30 (IST)

    Timing 1: 5.30am - 10.30am
    Timing 2: 11.00am - 4.00pm

    Sri Lanka
     GMT+5.30 (SLT)

    Timing 1: 5.30am - 10.30am
    Timing 2: 11.00am - 4.00pm

  • Korea
  • Korea
     GMT+9 (KST)

    Timing 1: 9.00am - 2.00pm
    Timing 2: 2.30pm - 7.30pm

  • Japan
  • Japan
     GMT+9 (JST)

    Timing 1: 9.00am - 2.00pm
    Timing 2: 2.30pm - 7.30pm

Session levels designed for you

Level 100

Sessions are focused on providing an overview of AWS services and features, with the assumption that attendees are new to the topic.

Level 200

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

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

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 Keynote Speakers

Craig Stires

Craig Stires,
Head of AI and Machine Learning, APJ, AWS

Olivier Klein

Olivier Klein,
Lead Technologist, APJ, AWS


Dean Samuels

Dean Samuels,
Lead Architect, ASEAN, AWS


Learn more about Machine Learning on AWS    


customers have chosen to
use AWS for machine learning

Leader in Gartner Magic Quadrant for Cloud AI Developer Services


new features


more productive using
Amazon SageMaker


of deep learning projects in
the cloud run on AWS

Frequently Asked Questions

1. Where is AWS Innovate hosted?
2. How to access the online event?
3. What is the price of attending AWS Innovate?
4. Who should attend AWS Innovate?
5. Can I get a confirmation of my AWS Innovate registration?
6. Are there sessions in other languages?
7. How do I get the certificate of attendance?
8. 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 instructions that you will need to access the platform.

Q: How to access the online event?
A: You will have to set a username and password to complete your registration and access the event on live day. If you have any questions, contact us at

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 make more accurate decisions.

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: Are there sessions in other languages?
A: We have sessions in Korean, Japanese, and Indonesian.

Q: How do I get the certificate of attendance?
A: As long as you complete watching 5 sessions or more, you should be eligible for the certificate of attendance. The certificate will be sent to your registered email address after the event has ended.

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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Craig Stires, Head of AI and Machine Learning, APJ, AWS

Craig Stires is the Head of AI and Machine Learning Sales Lead for Amazon Web Services, APJ. He has worked with some of the most innovative organizations across the region, as they architect AI and machine learning, and analytics platforms and become data-driven. When he first moved to Asia, in 2001, he was designing and implementing analytics solutions for Customer Engagement, Risk Management, and Operational Analytics. After several years, he founded a Startup in Thailand building predictive intelligence software. Following that, built the Big Data research practice for industry analytics firm IDC. After years of advising clients to build scalable, optimized, and business-ready analytics platforms, the time was right to get hands-on again. Moving to the world's largest cloud services provider has opened the door to work together with customers to build some of their most exciting visions.  

Olivier Klein, Lead Technologist, APJ, AWS

Olivier is a hands-on technologist with more than 10 years of experience in the industry and has been helping 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 AI, ML, and IoT spaces can help create new products, make existing processes more efficient, provide overall business insights, and leverage new engagement channels for consumers. 

Dean Samuels, Lead Architect, ASEAN, 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. 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.