AWS Innovate Online Conference – AI and Machine Learning Edition
Accelerate innovation, scale effortlessly, and unlock new possibilities with machine learning on AWS.
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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.


Provide 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.

 Download Agenda at a Glance »

Select a Track:

  • Accelerate AI/ML Journey
  • Use of AI/ML Services in Practical Use Cases
  • Build/Train/Deploy Machine Learning Models
  • AI/Ml Fundamentals, Framework, & Tools
  • AI/ML for Startups
  • AI/ML Insights for ISVs
  • AWS DeepRacer
  • Hands-on labs
  • Builders Zone
  •  French
  •  Portuguese
  •  Spanish
  • Accelerate AI/ML Journey
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    Accelerate AI/ML

    About the track

    Provide 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.

    Innovating 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, AWS Lead Technologist 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, AWS Lead Technologist, 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

    Duration: 45mins’s Use of AI/ML to Enhance the Customer Experience (Level 100) uses AI/ML in innovative and scaled ways toward transforming the way we operate and invent new customer experiences. In this session, targeted at senior business and technology decision makers, we share specific examples from's consumer/retail and other businesses to explain how AI/ML helps Amazon deliver the best customer experience possible while improving efficiency and lowering cost. We cover the insights and lessons learned across the cultural, process, and technology aspects of building and scaling ML capabilities in the organization.

    Speaker: Kieran Kavanagh, AWS Principal AI/ML Specialist Solution Architecht
    Duration: 30mins

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

    Canada as a Global Leader in AI/ML (Level 100)

    Canada has emerged as a leader in the advancement of artificial intelligence and is having a global impact in the field. This session walks technology leaders through the largest Canadian influences to AI/ML including the latest Canadian investment by AWS in this space: a new AI Solutions Engineering team based in Ottawa.

    Speaker: Shawn Gandhi, AWS Head of AI/ML Solutions
    Duration: 30mins

  • Use of AI/ML Services in Practical Use Cases
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    Use of AI/ML Services in Practical Use Cases

    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, improving safety and security, and increasing customer engagement with no machine learning skills required.

    Offer Your Customers Real-Time Personalized Recommendations (Level 100)

    Businesses of all sizes are trying to meet their users expectations of personalized recommendations in online shopping, online streaming content, and their digital media libraries of books, articles, music and podcasts. In this session we’ll share how customers have leveraged the personalization experience from, to offer product recommendations, similar item recommendations, and create re-ranking within each user experience. With no prior machine learning experience you can start creating A/B tests to see the impact of Amazon Personalize on increasing user engagement with your recommended products and content.

    Speaker: Divyesh Sah, AWS Solutions Architect
    Duration: 30mins

    Overcome Document Processing and Analysis Challenges at Scale (Level 100)

    Today, many financial services and healthcare organizations use documents to gather customer data via loan applications, rental agreements, patient intake forms, or medical claims forms. These documents hold valuable information the organization needs to process and understand. In this session, we will discuss how using Amazon Textract, Amazon Comprehend, and Amazon Augmented AI provide organizations with a machine learning solution, no experience required, to overcome document processing and analysis at scale.

    Speaker: Mudit Mangal, AWS Solutions Architect
    Duration: 30mins

    AWS Security: Where We’re Going and Where We've Been (Level 100)

    Here the latest security updates in the Well-Architected categories of detection, identity management, data protection, and incident response.

    Speaker: Shelbee Eigenbrode, AWS Sr. AI/ML Specialist Solution Architect
    Duration: 30mins

    Intelligent Search to Improve Workforce Productivity (Level 200)

    Amazon Kendra provides highly accurate and easy to use search powered by machine learning. This session will provide a high level overview of cognitive search, why it's important, and what it can do for your customers and employees. We will discuss on how Amazon Kendra delivers powerful natural language search capabilities to your websites and applications so your end users can more easily find the information they need.

    Speaker: Ryan Peterson, AWS Solutions Architect
    Duration: 30mins

    Bring the Power of Machine Learning to Your Fight Against Online Fraud (Level 200)

    Amazon Fraud Detector overcomes online fraud by using your data, machine learning (ML), and more than 20 years of fraud detection expertise to automatically identify potentially fraudulent online activity, allowing you to catch fraud faster. In this session, we will provide a deep dive into Amazon Fraud Detector, its use cases in your business, and how to quickly get started.

    Speaker: Tim Wu, AWS Solutions Architect
    Duration: 30mins

    Well-Architectured Framework for Machine Learning (Level 200)

    Artificial intelligence (AI) and machine learning (ML) help organizations improve outcomes with automation, predictive insights, natural language interactions, and data-driven decision making. The AWS Well-Architected Framework helps to build secure and resilient infrastructure for applications and workloads. In this whiteboarding session, learn how to design an ML application guided by the AWS Well-Architected Framework five pillars: operational excellence, security, reliability, performance efficiency, and cost optimization.

    Speaker: Shelbee Eigenbrode, AWS Sr. AI/ML Specialist Solution Architect
    Duration: 30mins

  • Build/Train/Deploy Machine Learning Models
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    Build/Train/Deploy Machine Learning 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, where to deploy and how to deploy. Working backwards from customer questions. Including topics on implementing and scaling ML models with MLOps.

    Prepare your Datasets at Scale using Apache Spark and SageMaker Data Wrangler (Level 300)

    Pandas doesn’t scale for large datasets. Apache Spark is an open source, distributed processing engine that scales to large datasets across a large number of cluster instances. In this session, I will demonstrate several ways to use Apache Spark on AWS to analyze large datasets, perform data quality checks, transform raw data into machine learning features, and train predictive models. I will also demonstrate how SageMaker Data Wrangler uses Apache Spark to detect bias in our datasets.

    Speaker: Chris Fregly, AWS Senior Advocate, AI/ML
    Duration: 30mins

    Orchestrate and Automate Machine Learning Workflows with SageMaker Pipelines (Level 300)

    Developing a high-quality ML model involves many steps. We typically start with exploring and preparing our data. We experiment with different algorithms and parameters. We spend time training and tuning our model until the model meets our quality metrics and is ready to be deployed into production. Orchestrating and automating workflows across each step of this model development process can take months of coding. In this session, you'll see how to create, automate, and manage end-to-end ML workflows using Amazon SageMaker Pipelines. We will create a reusable NLP model pipeline to prepare data, store the features in a feature store, fine-tune a BERT model, and deploy the model into production if it passes our defined quality metrics. 

    Speaker: Antje Barth, AWS Sr Developer Advocate
    Duration: 30mins

    Comparing Models in Production with Multi-Armed Bandits and Reinforcement Learning (Level 300)

    Using the popular Hugging Face Transformers open source library for BERT, I will train and deploy multiple natural language understanding (NLU) models and compare them in live production using reinforcement learning to dynamically shift traffic to the winning model. Next, I will describe the differences between A/B and multi-armed bandit tests including exploration-exploitation, reward-maximization, and regret-minimization. Last, I will dive deep into the details of scaling a multi-armed bandit architecture on AWS using a real-time, stream-based text classifier with TensorFlow, PyTorch, and BERT on 150+ million reviews from the Amazon Customer Reviews Dataset.

    Speaker: Chris Fregly, AWS Senior Advocate, AI/ML
    Duration: 30mins

    Reduce Training Time and Cost with SageMaker Debugger (Level 300)

    Manual debugging is a common productivity drain in the machine learning lifecycle. Identifying underperforming training jobs requires constant developer attention and deep domain expertise. Just as unit tests boost traditional software development, an automated ML debugging library can save time and money. Amazon SageMaker Debugger is a ML feature that provides a set of rules that automatically identify model- and performance-related issues and stops underperforming training jobs. Debugger automatically captures relevant data during training and evaluation and presents it for online and offline inspection. In this session, we walk through how to use the real-time training metrics and set up alerts so you can reduce troubleshooting time, training costs and improve model quality. 

    Speaker: Nathalie Rauschmayr, AWS Applied Scientist
    Duration: 30mins

    Standardize and Automate Your Feature Engineering Workflows with SageMaker Feature Store (Level 300)

    As a data scientist, you certainly spend a lot of time crafting feature engineering code. Indeed, given the experimental nature of this work, even a small project can lead to multiple iterations. Thus, you’ll often run the same feature engineering code again and again, wasting time and compute resources on repeating the same operations. In large organizations, this may cause an even greater loss of productivity, as different teams often run identical jobs, or even write duplicate feature engineering code because they have no knowledge of prior work. As models are trained on engineered datasets, it’s also imperative that you apply the same transformations to data used for prediction. This often means rewriting your feature engineering code (sometimes in a different language), integrating it in your prediction workflow, and running it at prediction time. This whole process is not only time-consuming, it can also introduce inconsistencies, as even the tiniest variation in your data transforms can have a large impact on predictions. In this hands-on session, you’ll learn how to solve all these problems with Amazon SageMaker Feature Store, and how to use it with both the SageMaker Studio user interface and the SageMaker SDK. You’ll also see how it works together with SageMaker Data Wrangler to simplify your end to end data preparation workflows.

    Speaker: Julien Simon, AWS Principal Advocate, ML/AI
    Duration: 30mins

    Scale your Large Distributed Training Jobs with Data and Model Parallelism Optimized for Amazon SageMaker (Level 300)

    As State-of-the-Art (SOTA) models for NLP and CV tasks grew in size considerably over past few years, it’s no longer feasible to train these large models on single machine due to memory and time constraints. In this demo session, learn about Amazon SageMaker Distributed Training (SDT) service which allows efficiently distribute training tasks across large compute clusters. Attendees will develop intuition about two approach to distribute your training tasks (model parallelism and data parallelism) and learn how to implement both approaches using SDT for popular NLP and CV model architectures.

    Speaker: Vadim Dabravolski, AWS Sr AI/ML Specialist Solution Architect
    Duration: 30mins

  • AI/Ml Fundamentals, Framework, & Tools
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    AI/Ml Fundamentals, Framework, & Tools

    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. MLOps with containers technologies. In addition, sessions with hands-on on machine learning tools and devices – AWS DeepRacer, AWS DeepComposer and AWS DeepLens.

    Build your Own Defect and Anomaly Detection Models Without ML Skills  (Level 200)

    How can development teams add smart capabilities to business applications without any ML skills? In this hands-on session we will focus on two specific use cases related to anomaly detection. We'll dive into the practical steps of how to identify product defects in images and how to detect outliers and issues in business metrics.

    Speaker: Alex Casalboni, AWS Sr Developer Advocate
    Duration: 30mins

    Deploy State of the Art ML Models and Solutions in a Single Click (Level 200)

    There are many challenges in getting started with machine learning (ML). Developers who are new to this field often struggle with importing a model from a popular model zoo and deploying it to an API endpoint. Additional components are also needed to launch a working ML application, including API gateways, serverless compute, object storage, ETL streams, dashboards, and authentication. Thus, the end-to-end process of building a solution can take months or longer for new ML users. In this talk, I will show you can easily and quickly bring ML applications to market. I will demonstrate how to deploy solutions using out-of-the-box ML models, and how to customize them for a specific business problem. With just a few clicks, I will show you how to launch ML solutions preconfigured with all AWS resources required for production, including a CloudFormation template and a reference architecture.

    Speaker: Sebastian Stormacq, AWS Principal Developer Advocate
    Duration: 30mins

    Build Quality ML Models Quickly & Easily with Amazon SageMaker Autopilot (Level 300)

    Building machine learning (ML) models has traditionally required a binary choice. On one hand, you could manually prepare the features, select the algorithm, and optimize the model parameters in order to have full control over the model design. However, this approach requires deep ML expertise. On the other hand, if you don’t have that expertise, you could use an automated approach (AutoML) to model generation that takes care of all of the heavy lifting, but provides very little visibility into how the model was created. While a model created with AutoML can work well, you may have less trust in it because you can’t understand what went into it, you can’t recreate it, and you can’t learn best practices which may help you in the future. In this session I am going to show you how SageMaker Autopilot eliminates this choice, allowing you to automatically build machine learning models without compromises, explore different solutions to find the best model, and then directly deploy the model to production with just one click.

    Speaker: Ramu Ponugumati, AWS Sr. Technical Acct Mgr
    Duration: 30mins

    Automate Code Reviews, Performance Recommendations and Operational Insights (Level 200)

    A better understanding of your code base helps reduce overall costs, improves non-functional behaviours 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 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 concurrency bugs and automating performance reviews through application profiling to identify the lines of expensive object recreation, usage of inefficient libraries, too much logging, concurrency issues, etc that improves code performance for applications in production. This session will also cover 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 the overall application availability, operational performance, reduce expensive downtime and provides Operational insights.

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

    Select the Right ML Instance for your Training and Inference Job (Level 200)

    AWS offers a breadth and depth of machine learning (ML) infrastructure for training and inference workloads that you can use through either a do-it-yourself approach or a fully managed approach with Amazon SageMaker. In this session, explore how to choose the proper instance for ML training and inference based on model size, complexity, throughput, framework choice, inference latency and portability requirements. Join this session to compare and contrast compute-optimized CPU-only instances, such as Amazon EC2 C4 and C5; high-performance GPU instances, such as Amazon EC2 G4, P3, and P4d; cost-effective variable-size GPU acceleration with Amazon Elastic Inference; and high performance/cost with Amazon EC2 Inf1 instances powered by custom-designed AWS Inferentia chips.

    Speaker: Shashank Prasanna, AWS Senior Advocate, AI/ML
    Duration: 30mins

    Detect Potential Bias in your Datasets and Explain how your Models Predict using SageMaker Clarify (Level 300)

    As ML models are built by training algorithms that learn statistical patterns present in datasets, several questions immediately come to mind. First, can we ever hope to explain why our ML model comes up with a particular prediction? Second, what if our dataset doesn’t faithfully describe the real-life problem we were trying to model? Could we even detect such issues? Would they introduce some sort of bias in imperceptible ways? These are not speculative questions at all, and their implications can be far-reaching. Unfortunately, even with the best of intentions, bias issues may exist in datasets and be introduced into models with business, ethical, and legal consequences. It is thus important for model builders and administrators to be aware of potential sources of bias in production systems. In addition, many companies and organizations need ML models to be explainable before they can be used in production. In fact, some regulations explicitly require model explainability for consequential decision making. In this hands-on session, you’ll learn how Amazon SageMaker Clarify can help you tackle bias and explainability issues, and how to use it with both the SageMaker Studio user interface and the SageMaker SDK. You’ll also see how it works together with SageMaker Model Monitor to track bias metrics over time on your prediction endpoints.

    Speaker: Julien Simon, AWS Principal Advocate, ML/AI
    Duration: 30mins

  • AI/ML for Startups
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    AI/ML for Startups

    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 for AI/ML startups.

    How to Accelerate Your Models to Production with SageMaker

    Amazon SageMaker includes modules that can be used together or independently to build, train, and deploy your machine learning models. In this technical deep dive, we'll demonstrate SageMaker's advanced features that help you train and iterate on your machine learning models faster. You'll learn operational techniques to turn your machine learning research project into a production-ready micro-service.

    Speaker: Sean Wilkinson, AWS Machine Learning Specialist Solutions Architect
    Duration: 30mins

    A Developer’s Guide to Choosing the Right GPUs for Deep Learning

    As a deep learning developer or data scientist, you can choose from multiple GPU EC2 instances types based on your training and deployment requirements. Attend this talk to learn how you can choose the right GPU instance on AWS to meet your target performance goals. Take home guidance on maximizing resource utilization to find performance bottlenecks and reduce overall training and inference costs.

    Speaker: Shashank Prasanna, AWS Senior Advocate, AI/ML
    Duration: 30mins

    Confessions of an AI/ML Startup Founder 

    Through triumphs and missteps, founders learn invaluable lessons and build new skillsets, leading them to greater heights. You are invited to learn from some of the world's top AI/ML founders for practical lessons, advice and stories that every founder should know. Explore state of the art ML, discover how to avoid common AI/ML mistakes, and learn how to incorporate diversity and sustainability practices into your ML startup.

    Speaker: Ari Kalfayan, AWS Senior Business Development Manager
    Duration: 30mins

    Scaling Your Startup: How to Build an Incredible Business by Leveraging Existing Tools 

    Transitioning from a single engineer on a single laptop to a team of engineers can be painful. It's essential to leverage cloud services and tools to not only scale your product, but your team. In this session, learn how to: set up a data lake and implement into an ML experimental workflow, understand just enough data infrastructure and ML to be effective, and prepare an end-to-end workflow to easily share the workload.

    Speaker: Rob Ferguson, AWS Principal Machine Learning Business Development Manager for Startups and Venture Capital
    Duration: 30mins

    Build AI-Powered Applications Without Any Machine Learning Expertise

    For startups just getting their feet wet in machine learning, we’ll showcase the fastest ways to add intelligence into your products and applications—without hiring an entire ML team. Whether you’re improving your customer support workflow or automatically reviewing your code for security vulnerabilities, you’ll learn the ready-made workflows to help you improve business outcomes and raise that next round.

    Speaker: Allie K. Miller, AWS Global Head of ML Business Development for Startups and Venture Capital
    Duration: 30mins

    Next Generation Cloud Platforms for Autonomous Development

    Companies focused on Autonomous Vehicle (AV) development require extensive computing resources to accelerate the development process. Join this session to review AWS AV solutions for the toolchain including data ingest, management, labeling, simulation, and model training. You'll learn development and validation strategies to help you accelerate AV deployments, while reducing costs and time-to-market. 

    Speaker: Vijitha Chekuri, AWS Principal Specialist, A.C.
    Duration: 30mins

  • AI/ML Insights for ISVs
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    AI/ML Insights for ISVs

    About the track

    Provide Independent Software Vendor (ISV) customers with insights on how AI and ML can help solve business challenges and innovate faster for their customers. ISV customers will have the opportunity to learn from their peers in companies on the cutting edge of machine learning innovation. This is a great opportunity to gain a better understanding of how other AWS customers are leveraging the benefits of machine learning today and thinking about the applications of tomorrow.

    Improving ML Models with Access to More Data: Using SageMaker Data Wrangler to Integrate Multiple Data Sources Together and Power Innovation and Growth (Level: 200)

    Data preparation is a critical step in the end-to-end machine learning process. Amazon SageMaker Data Wrangler is purpose-built for ML-specific data preparation workloads. In this session we discuss a new capability in Data Wrangler which allows users to quickly and easily connect to their data in Snowflake. This new capability resulted from an increased collaboration with Snowflake to help SageMaker users easily access and prepare their data from Data Wrangler and further expand the set of use cases they develop ML models for.

    Sri Chintala, Product Manager, Snowflake
    Ajai Sharma, AWS Senior Product Manager, AI

    Duration: 30mins

    Unlocking Value with Customer-Specific ML Models at Scale: Infusing ML into your Platform to Improve Customer Success (Level: 300)

    As an ISV, building customer-specific experiences enables you to expand revenue streams, reduce churn, and increase customer satisfaction. Machine learning can power this personalized experience, but how do you build customer-specific ML models at scale if you have thousands of customers? Join us to learn how Genesys leveraged Amazon SageMaker to build its Predictive Engagement offering that trains and deploys thousands of personalized ML models completely automatically, while helping them minimize their cost.

    Julianne Chaloux, Senior Software Engineer at Genesys
    Kosti Vasilakakis, AWS Sr. Business Development Manager

    Duration: 30mins

    Simplifying ML Model Integrations: Leveraging the ML Models you Built with SageMaker Anywhere (Level: 200)

    Organizations that manage to achieve mature forms of predictive analytics gain the competitive advantage of having foresight into their business. In this session, we will showcase how Tableau and AWS joined forces to enable that foresight by democratizing access to ML models for the BI users. We will walk you through how you can leverage the ML models built on SageMaker, right within your Tableau dashboards, and thus not only visualize the past, but also instantly predict the future. We will also discuss our integration journey, and the lessons learned when pursuing to bridge the gap between your data scientists and your business teams.

    Madeleine Cornelli, Product Manager, Tableau
    Kosti Vasilakakis, AWS Sr. Business Development Manager

    Duration: 30mins

    Putting ML Into The Hands Of Every User: Enabling End-Customers to Build and use ML Models with SageMaker Autopilot Right Within your own Platform (Level: 100)

    With rich data available from a variety of sources, enterprises and business analysts are increasingly turning to data platforms such as Domo to integrate and analyze data from disparate sources. To gain insights from this data, analysts want the ability to create predictive models based state of the art machine learning algorithms, right where their data is. Domo AutoML, powered by Amazon SageMaker Autopilot, makes it easy to quickly create, use, and make predictions with highly accurate ML models from within Domo. Join us to learn more about how SageMaker Autopilot automatically builds, trains, and tunes the best machine learning models based on data managed by Domo, while allowing users to maintain full control and visibility, without leaving the Domo platform.

    Sean Thompson, IT Director, Freddy’s
    Tushar Saxena, Principal Product Manager, AWS
    Ben Ainscough, Ph.D., Head of AI and Data Science Product, Domo

    Duration: 30mins

    Talkdesk shares how to add intelligence to your existing contact center with AWS Contact Center Intelligence (CCI) (Level: 200)

    Learn how your organization can leverage AWS Contact Center Intelligence (CCI) solutions to improve customer experience and reduce cost with AI. We will explore how AWS CCI solutions can be built easily through an expanding network of partners to provide self-service interactions, live and post-call analytics and agent assist on existing contact center systems. AWS Partner, Talkdesk will share how they improve customer experience and tackle challenging business problems such as improving agent effectiveness, and automating quality management in enterprise contact centers.

    Ben Rigby, VP AI, Talkdesk
    Paul Lasserre, Global Solutions Lead AI/ML, AWS

    Duration: 30mins

    Use AWS ML Services to Analyze your Videos to Create New Insights, Automate Work, and Increase Content Value with AWS Media Intelligence (Level: 200)

    In this session, dive into the most common problems facing organizations who produce, process, or archive large amounts of video and audio, and how they can better review, search, and analyze content at scale with AWS image and speech AI services. AWS and its technology partner Synchronized will share their experience helping customers from enhancing the viewer experience by streamlining operational and compliance media tasks, to more effectively monetizing content and optimizing content archives. In particular, learn how Synchronized accelerated innovation on behalf of leading TV channels using Amazon Rekognition & Rekognition Custom Labels, and Amazon Transcribe to analyze Video-on-demand (VoD) content and automating the creation of Smart thumbnails.

    Paul Lasserre, Global Solutions Lead AI/ML, AWS 
    Guillaume Doret, CEO, Synchronized TV

    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 trainers online to understand how to get started, get certified and build your own learning path moving forward.

    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.

    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.

    Reinforcement learning (Level 200)
    In this lab, learn how to build reinforcement learning (RL) models using Amazon SageMaker RL, which includes prebuilt RL libraries and algorithms that help you achieve faster turnaround times and improve the results of your RL experiments.

    Customer churn prediction (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. In this lab, we explain how to use machine learning (ML) to predict customer churn. We also discuss how to incorporate the costs associated with prediction mistakes to determine the financial outcome of using ML.

    Virtual contact center (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.

    Movie batch recommendations (Level 200)
    In this lab, we walk you through how to train and create batch recommendations using Amazon Personalize. Learn how you can perform tasks such as generating recommendations for a large number of users that will be used later for batch-oriented workflows like sending emails or notifications.

    Sentiment analysis web application (Level 200)
    In this lab, we demonstrate how to add artificial intelligence (AI) and machine learning (ML) cloud service features to your web application with React and the AWS Amplify Framework.

  • Builders Zone
  • builder-zone-icon-v2

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

    Multilingual omnichannel contact center (Level 300)
    This project recognizes the contact center industry’s critical language barrier issue between agents and customers participating in a live chat conversation. It would perform real-time translation of the chat conversation between the agent and the customer and provide a chat-based output to both sides according to their desired languages (the team is also performing research to try this solution for voice-based conversations). The project leverages Amazon Connect to provide a seamless contact center experience, as well as Amazon Translate, Amazon Polly, and Amazon Transcribe.

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

    Automated corrosion detection using machine learning (Level 300)
    Visual inspection of industrial environments is a common requirement across heavy industries, and as a result, experts often have to perform manual inspections in adverse environments that put them at risk. Many of these industries deal with huge metal surfaces that are subject to corrosion, which poses a serious risk and huge financial impact. This demonstration showcases a machine learning approach to corrosion detection that helps visualize corroded areas. Learn how AWS Step Functions is used to create Amazon SageMaker machine learning models and deploy them for inference within a web application built with AWS Amplify and Amazon CloudFront.

    Aravind Kodandaramaiah, Global Accounts Solutions Architect - Prototyping, AWS
    Mehdi Far, Senior Solutions Architect - AI/ML, AWS

    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

  •  French
  •  Portuguese
  •  Spanish

Conference Time

  • United States & Canada
  • Latin America
  • United States & Canada
  • Pacific Time
     GMT -8 (PST)

    Timing: 9.00am - 2.00pm

    Mountain Time
     GMT -7 (MST)

    Timing: 10.00am - 3.00pm

    Central Time
     GMT -6 (CST)

    Timing: 11.00am - 4.00pm

    Eastern Time
     GMT -5 (EST)

    Timing: 12.00pm - 5.00pm

  • Latin America
  • México
     GMT-6 (CST)

    Timing: 9.00am - 2.00pm

    Puerto Rico
     GMT-4 (AST)

    Timing: 11.00am - 4.00pm

    Colombia, Peru
     GMT-5 (COT)

    Timing: 10.00am - 3.00pm

    Argentina, Chile, Paraguay, Uruguay
     GMT-3 (ART)

    Timing: 12.00pm - 5.00pm

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.

Keynote speaker

Denis V. Batalov, PhD

Denis V. Batalov, PhD.
AWS Tech Leader for AI/ML

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 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 French, 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.

Get Started with Amazon SageMaker

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Denis V. Batalov, PhD, AWS Tech Leader for AI/ML

As a 15-year Amazon veteran and a PhD in Machine Learning, Denis worked on such exciting projects as Search Inside the Book, Amazon Mobile apps and Kindle Direct Publishing. Since 2013 he has helped AWS customers adopt AI/ML technology as a Solutions Architect. Currently, Denis is a Worldwide Tech Leader for AI/ML responsible for the functioning of AWS ML Specialist Solutions Architects globally. Denis is a frequent public speaker, you can follow him on Twitter: @dbatalov