AWS Innovate - Data & AI/ML

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Sponsor: Nvidia

 February 22, 2023

 Asia Pacific & Japan

Reimagine new possibilities with big data and machine learning

Today, many organizations are using AI/ML to deliver greater business value-from boosting productivity, enhancing customer experiences, making better decisions faster to generating new revenue opportunities and improving operational efficiencies.

Join us for AWS Innovate - Data and AI/ML Edition and learn how you can unlock the power of AI/ML to achieve more for your organization. At this free online conference, learn the latest from AWS experts and get step-by-step guide on using AI/ML to drive fast, efficient, and measurable results.

Agenda (Asia Pacific & Japan)

Take your AI/ML skills to the next level today! Get hands-on and step-by-step architectural and deployment best practices to help you build better, innovate faster, and deploy at scale. Whether you are just getting started with AI/ML, an advanced user, or simply curious about AI/ML, we have a specific track for your level of experience and job role.

Agenda overview
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Sessions

  • Opening keynote
  • Opening keynote

    Opening keynote

    Innovate faster: Reinvent your organization with data and machine learning (Level 100)
    Data is at the center of every application, process, and business decision and the foundation to delivering greater value. Organizations who are successful in extracting insights from their data are able to deliver accurate predictions, reduce operational overheads, invent more compelling customer experiences, and uncover new opportunities. In this session, uncover how technologies like machine learning and analytics can unlock opportunities that were either too difficult or impossible to do before, enabling organizations with data-driven insights to solve business challenges, accelerate innovation, and stay ahead of the competition.

    Speakers:
    Dean Samuels, Chief Technologist, ASEAN, AWS
    Kris Howard, Head of Dev Relations, APJ, AWS

  • Data-driven organizations of tomorrow
  • Data-driven organizations of tomorrow

    Data-driven organizations of tomorrow

    About the track

    Get inspired and learn how organizations are using AWS to solve business challenges, optimize business performance, and innovate faster. Start leveraging your data as a strategic asset and reinvent your organization with data and AI/ML today.

    Data-Driven Enterprise: Vision to Value (Level 100)
    Organizations are looking to derive greater value from their data to increase agility, improve efficiency, and accelerate innovation. While data is abundant and growing rapidly, just producing or storing a lot of it doesn’t automatically create value. Value is realized by creating a culture and operating model that use the data to invent on behalf of customers using actioned insights, analytics, and AI/ML. However, cultural challenges, outdated governance models, organizational silos, and legacy execution approaches stand in the way of realizing this vision. Join this session to learn the strategies from two former CXOs on how they worked to create a data-driven culture and overcame the challenges to turn their vision into reality.

    Speaker: John Clark, Enterprise Strategist, AWS
    Duration: 30mins


    Building a smarter organization powered by data and machine learning (Level 100)
    Many organizations know they need AI/ML to create unique competitive advantage, drive better customer engagement, and deliver desired business outcomes. While some are reaping the benefits from the transformative impact of AI/ML, others are searching for answers on where to start. This session covers how to apply AI/ML and make your digital transformation a reality. We share the data network effect and the areas that successful organizations master to bring about greater value from the data, powered by machine learning to make their vision a reality.

    Speaker: Tom Godden, Enterprise Strategist, AWS
    Duration: 30mins


    Driving sustainability with data and AI/ML at Amazon and Climate Pledge Arena (Level 100)
    Amazon Climate Pledge Arena is designed to be the first net-zero carbon certified arena in the world and set a new sustainability bar for the sports and events industry. In this session, we share how AWS Professional Services and Amazon Sustainability teams use AWS services to ingest and analyze data such as energy, water, and air-quality and build forecasting models. We share how the teams work collectively to build solutions to help make Climate Pledge Arena the most progressive, responsible, and sustainable arena in the world.

    Speakers: 
    Rahul Sareen, Global Practice Manager, Sustainability, AWS
    Rob Johnson, VP Sustainability & Transportation, Climate Pledge Arena

    Duration: 30mins

  • Rethink Possible: Accelerate AI & ML innovations
  • Rethink Possible: Accelerate AI & ML innovations

    Rethink Possible: Accelerate AI & ML innovations

    About the track

    Find out how AI and ML services are applied to applications and used in real-world use cases across industries and organizations.

    Getting started on your ML journey: Strategies to deliver AI/ML at scale (Level 100)
    AI and ML hold the promise of transforming industries, increasing efficiencies, and driving innovation. The key to machine learning success is scale. This session covers 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 share how customers worked with AWS to align their teams in introducing ML and providing their teams the right technical skillset to deliver business outcomes. Learn how to create strong AI/ML product and engineering teams, aligned to organization’s shared goals, to deliver a roadmap of innovation and value.

    Speaker: Naomi Teng, AI/ML Specialist, APJ, AWS
    Duration: 30mins


    Transform your business with AI/ML: How to create a competitive advantage in your organization by leveraging AI/ML latest trends (Level 100)
    AI/ML techniques are increasingly important fundamentals for organizations looking to transform and deliver their objectives. However, applying AI/ML in the right place is not easy. Join this session to learn how to apply practical and proven machine learning use cases to quickly achieve real business impact. We share the AWS AI/ML suite of services that allow you to build transformative products without requiring prior machine learning expertise. Uncover how to create your own flywheel of AI powered transformation based on both existing and emerging technologies to conceptualize new opportunities, achieve competitive advantage and deliver organizational outcomes.

    Speaker: Nieves Gracia, AI & ML BD lead, APJ Public Sector, AWS
    Duration: 30mins


    Rethink machine learning for regulated industries (Level 100)
    Reproducibility, traceability, explainability have become the fundamental requirement in the machine learning lifecycle for regulated industries. But building a data science platform for a bank or the government to supports this lifecycle is no easy feat as it requires a deep set of capabilities and experience. AWS provides a set of services and solutions to create a secure, governed, and compliant machine learning environment, that does not compromise on data science teams’ agility. Join this session to learn how to close the gap between the traditional machine learning lifecycle and regulated industry requirements. We share how AWS provides you the solutions, best practices, programs, and resources to help you build a successful data science and machine learning platform on AWS.

    Speaker: Juan Bedoya, Public Sector Solutions Architect, AWS
    Duration: 30mins


    Applying AI/ML in financial services (Level 100)
    Financial services institutions (FSI) have been investing in AI/ML to create opportunities and deliver business outcomes by improving processes such as fraud detection, speed up claims processing or extract data quickly to perform business operations. We provide the overview on how AI/ML services from AWS are designed to meet the needs of financial institutions of all sizes. We showcase how to leverage AI/ML technologies to build an automated document processing workflow at scale, enabling financial institutions to reduce manual effort, process documents faster and with higher accuracy.

    Speaker: Gaurav Arora Principal Solutions Architect, AWS
    Duration: 30mins


    Delivering multimodal customer engagement in financial services (Level 100)
    The widespread adoption of mobile services, new digital native market entrants, adapting to generational shifts, and critical industry requirements on regulatory compliance make it challenging for financial services institutions to deliver a personalized, consistent and seamless customer experience from different channels. This session showcases how you can easily build a multimodal customer experience with AWS AI and machine learning. Learn how to create the personalized mobile, web and text-first activated experiences for both inbound and outbound customer engagement, by leveraging contextualized data while maintaining a single conversation across touchpoints.

    Speakers:
    Akash Jain, Head - FSI GTM Solutions Architect, APJ, AWS
    Rahul Kulkarni, Senior Partner Solutions Architect, AWS India

    Duration: 30mins


    Build secure, well-governed machine learning environments on AWS (Level 100)
    Whether your organization is starting on its AI/ML journey, or has a large number of projects in production, it is vital to have secure environments that protect your data. In this session, we share how you can organize, standardize, and expedite the provisioning of governed ML environments by leveraging the AWS security best practices and meet the strict security requirements of ML workloads.

    Speaker: Tony Fendall, Principal, Solutions Architect, AWS
    Duration: 30mins


    Deep learning on AWS with NVIDIA: From training to deployment (Level 200)
    Over the past decade, NVIDIA has been able to illustrate the effectiveness of its GPUs across the board for both deep learning training and inference. As these models become larger, the inherent need to scale up for training and scale out for deploying such large models has become a necessity. In this session, we will walk through a few NVIDIA software stacks for efficient distributed training as well as streamlined deployment and dive deep into how Amazon adopts them for some of their most demanding workloads.

    Speaker: Michael Lang, Solutions Architecture Manager, APAC South, NVIDIA
    Duration: 30mins

  • AI/ML use case solutions track 1
  • AI/ML use case solutions track 1

    AI/ML use case solutions track 1

    About the track

    Discover the various machine learning integration services available on AWS to help you build, deploy, and innovate at scale. We also focus on how AI services are applied to common use cases such as personalized recommendations, adding intelligence to your contact center, and improving customer experience.

    Build centralized smarter search capabilities from distributed data stores with Amazon Kendra (Level 200)
    How can you get the most accurate and specific answers in search queries when the answers may require you to sieve through large volumes of distributed data sources? In this session, we show how to use Amazon Kendra, an intelligent search solution to get straightforward answers. Learn how you can bridge a number of third-party tools, sources, and products to create unified and smarter data search capabilities, improve cross-team knowledge sharing, enhance sales, and customer support services, which makes it much easier to get the information you need.

    Speakers: 
    Sam Gordon, Senior Cloud Architect, AWS
    Ed Fraga, Cloud Architect, AWS

    Duration: 30mins


    Create a unified text and image search application with analytics and ML (Level 200)
    While text and semantic search engines has enabled many organizations to search for information quickly, organizations that offer unified text and image search engines can provide competitive advantage and revenue streams by offering their customers the flexibility to show physical examples or images to describe the items in the search engines. This session showcases how to build a ML-powered search engine to easily retrieve and recommend products based on text or image queries. Learn how to use Amazon SageMaker to host and manage the pre-trained Contrastive Language–Image Pre-training (CLIP) model, and run visual search from a query image. We also share how to use easy to deploy, operate, and scale OpenSearch clusters and the other AWS services to build this end-to-end application.

    Speaker: Kevin Du, Senior ML Data Lab Solutions Architect, AWS
    Duration: 30mins


    Scalable data preparation & ML using Apache Spark on AWS (Level 200)
    Analyzing, transforming and preparing large amounts of data is a foundational step of any data science and ML workflow. This session shows how to build end-to-end data preparation and machine learning (ML) workflows. We explain how to connect Apache Spark, for fast data preparation in your data processing environments on Amazon EMR and AWS Glue interactive sessions from Amazon SageMaker Studio. Uncover how to access data governed by AWS Lake Formation to interactively query, explore, visualize data, run and debug Spark jobs as you prepare large-scale data for use in ML.

    Speaker: Suman Debnath, Principal Developer Advocate, Data, AWS
    Duration: 30mins


    Automate data extraction and analysis: Build an intelligent document processing solution 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, costly and may produce low-accuracy extraction results. In this session, learn how to build an end-to-end intelligent document processing solution to overcome the legacy document processing challenges, enabling you to extract structured data, redact sensitive information, and deploy automated document processing workflow at scale.

    Speaker: Abhijit Kalita, Senior AI/ML Evangelist, AWS
    Duration: 30mins


    Correct OCR errors and tune data extraction (Level 300)
    Many business workflows still receive information from print media and they use OCR technology to convert text images into text data for organizations for data driven decisions. However, this is becoming challenging with growing volume of data to process, and more users demand for highest confidence scores. In this session, we cover how to integrate Amazon Textract, with custom models and human-in-the-loop reviews on Amazon SageMaker. We showcase how to use modern, open-source models from Hugging Face to help correct OCR error patterns and support structure extraction, even on diverse datasets.

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


    From accuracy to business case: Building a successful demand forecasting PoC (Level 200)
    Forecasting future demand accurately with AI/ML has numerous benefits across different functions including increasing sales, improving capacity utilization and inventory turn-over, and enhancing customer experience. But many face challenges in justifying the value and implementing demand forecasting systems into production. This session shows you the step by step workflow to build a rapid prototyping for ML-based forecasting system using Amazon Forecast. We showcase the different ways for measuring the real business value of demand forecasting models while allowing flexibility in experimentation.

    Speaker: Julia Ang, Associate Solutions Architect, AWS
    Duration: 30mins


    Personalize customer engagements with marketing automation (Level 200)
    When it comes to customer communications, it is not surprising that personalization is the best way to ensure engagement with customers in the long run. Customers are more likely to give their attention to content that is tailored to their needs. In this session, we showcase how to use Amazon Pinpoint journeys to provide personalized multi-step customer experience based on audience attributes and behaviors; and how to use Amazon Personalize to ensure that the communication content is always specific and personalized for the recipient.

    Speaker: Pierre Semaan, GTM Strategy and Solutions Lead, SMB, APJ, AWS
    Duration: 30mins

  • AI/ML use case solutions track 2
  • AI/ML use case solutions track 2

    AI/ML use case solutions track 2

    About the track

    Discover the various machine learning integration services available on AWS to help you build, deploy, and innovate at scale. We also focus on how AI services are applied to common use cases such as personalized recommendations, adding intelligence to your contact center, and improving customer experience.

    Improve customer experience with ML powered contact centers (Level 300)
    Your contact center is the biggest touchpoint between you and your customers, and every engagement can provide your team with powerful insights. In this session, we demonstrate the integration of Amazon Connect with AWS analytics and ML services, so you can use the self-service configuration tools to accomplish in days, instead of what would have taken you months to build. Learn how this end-to-end cloud center solution built on AWS allows you to surface valuable insights from every customer engagement including real-time churn predictions so you can improve your customer experience.

    Speakers: 
    Nelson Martinez, Senior Technical Account Manager, Productivity Apps, AWS
    Melanie Li, Senior Technical Account Manager, Analytics, AWS
    Partha Sarathi Sahoo, Senior Technical Account Manager, Analytics, AWS

    Duration: 30mins


    Breaking language barriers with AWS AI services (Level 200)
    Amazon brings natural language processing, speech recognition, text to speech, and machine translation within the reach of every developer. API-driven application services enable data scientist and developers to easily plug pre-built artificial intelligence functionality into their applications and to automate workflows. In this session, we explain how to build the next generation of intelligent apps that hear, speak, and understand the world around us.

    Speakers: 
    Jyoti Sharma, Prototyping Engineer, AWS India
    Arun Balaji, Principal Prototyping Engineer, AWS India

    Duration: 30mins


    Build engaging live video streaming experience and optimize revenue opportunities with AWS (Level 200)
    Most video streaming content providers are looking to deliver premium viewing experience, boost real time viewer engagement, and improve monetization of their video assets. In this session, learn how to run high-quality, low-latency, and resilient live streams on AWS. We demonstrate how to use Amazon Rekognition to improve content engagement rates by generating buyer’s catalogue automatically from an IVS livestream. By adding AI/ML into the workstream, this enables viewers to purchase the products or services that appear during the livestream.

    Speaker: Ally Yong, Solutions Architect, AWS
    Duration: 30mins


    Intelligent media analytics with machine learning (Level 200)
    Media assets, such as audio and video, can be used to increase discoverability and drive greater user engagement and satisfaction. However, managing, analyzing, and monitoring media content is both complex and expensive. This session demonstrates how to use AWS AI services and Amazon SageMaker for better content search and discovery, increase accessibility through captioning and localization, and uncover new content monetization. We also show to use fully-managed image, video, text, and speech moderation APIs and automated machine learning to improve compliance and brand safety for you and your customer.

    Speakers: 
    Sakthi Srinivasan, Engagement Manager, Prototyping, AWS India
    Arun Balaji, Principal Prototyping Engineer, AWS India

    Duration: 30mins


    Build an end-to-end credit card fraud detection system (Level 300)
    As we move towards cashless society, the ability to detect fraudulent card transactions accurately and quickly has become increasingly important, because false positives can result in negative customer experiences. In this session, uncover how to build end-to-end credit card fraud detection system with Amazon SageMaker. Learn how to train mathematical models in the cloud for detecting fraudulent card payment fraud with an approach that is more agile and cost-efficient. We demonstrate how you can integrate this model with your business applications using APIs and build reporting dashboards with Amazon QuickSight, a fast, cloud-powered BI service that makes it easy for everyone in an organization to get insights from their data through rich, interactive dashboards.

    Speaker: Indrajit Ghosalkar, Senior Solutions Architect, AWS
    Duration: 30mins


    Combat account takeover fraud with AWS (Level 300)
    Every year, many user accounts are compromised by different techniques such as credential stuffing, phishing, and social engineering, leading to account takeover (ATO) fraud. Apart from financial losses, ATO fraud has effects on customer experience, brand loyalty, and reputation. In this session, we explain how AWS Web Application Firewall enables you to proactively stop account takeover attempts at the network edge, prevent unauthorized access that may lead to fraudulent activities, and notify users in advance to take preventive action. We also showcase how to use additional ways to protect your application with machine learning using Amazon Fraud Detector, a fully managed service that enables you to build, deploy, and manage custom fraud detection ML models quickly without previous ML experience.

    Speakers: 
    Julian Ju, Senior Edge Specialist Solutions Architect, AWS
    Ram Cholan, Senior Edge Specialist Solutions Architect, AWS

    Duration: 30mins


    Extracting meaningful radiology insights from natural language processing and ML (Level 300)
    The insights needed to optimize the use of scarce and in-demand clinical resources are often hidden plain sight within unstructured clinical reports. This session explains how to integrate machine learning and analytics technology into their applications and automate processes to optimize the use of clinical resources. We show the use of near-real-time Apache Spark pipeline, with Amazon Comprehend Medical, to capture radiology examinations as they are added to the hospital's clinical data repositories. Learn how to classify natural language clinical notes, and translate the clinical entities into relational views built on standard SNOMED Clinical Terminology. We conclude by showcasing how general-purpose visualization and analysis tools can enable your users to access the data insights.

    Speaker: Craig Roach, Principal Solutions Architect, AWS
    Duration: 30mins

  • Build, train, and deploy ML models track 1
  • Build, train, and deploy ML models track 1

    Build, train, and deploy ML models track 1

    About the track

    Learn how to build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows

    Get started with Amazon SageMaker in minutes (Level 200)
    Amazon SageMaker provides every developer, business analyst, and data scientist with the ability to build, train, and deploy machine learning models for any use case with fully managed infrastructure, tools, and workflows. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. This session dives deep into the technical details of each of the modules of Amazon SageMaker which showcases the capabilities of the platform.

    Speaker: Pauline Kelly, Solutions Architect, AWS
    Duration: 30mins


    Transform nested transactional data for ML with AWS Glue DataBrew and Amazon SageMaker (Level 200)
    In many industries, data comes from various sources in structured, semi-structured, and unstructured formats. For semi-structured data, one of the most common lightweight file format is JSON. However, due to the complex nature of JSON data type, it often includes nested key-value structure and is difficult to be used directly in ML tasks. In this session, we discuss how to leverage AWS Glue DataBrew to unnest the data, handle sensitive information, and ensure data quality for ML data preparation. We share how to use Amazon SageMaker no-code solution to automatically train ML models with the processed data to unlock actionable insights quickly.

    Speakers: 
    Melanie Li, Senior Technical Account Manager, AI/ML, AWS
    Partha Sarathi Sahoo, Senior Technical Account Manager, Analytics, AWS

    Duration: 30mins


    Build accurate models combining diverse data types with AutoGluon on Amazon SageMaker (Level 300)
    Real-world machine learning use-cases often involve data in many forms. In this session, we cover the overview of Amazon SageMaker JumpStart which automatically trains and tunes hundreds of ML models and helps you pick the best model for your use case. We demonstrate how to use AutoGluon, an open-source library for AutoML on Amazon SageMaker to build your high-quality model. We also share proven techniques, best practices and tools for diving deeper with custom multi-modal ML.

    Speaker: Seema Gupta, Senior Solutions Architect, AWS
    Duration: 30mins


    Train ML models quickly and cost-effectively with Amazon SageMaker (Level 200)
    Training machine learning models at scale often requires significant investments. In this session, we show how Amazon SageMaker enables you to reduce time and costs to train and tune machine learning (ML) models without the need to manage infrastructure. Learn how to use models using built-in tools to manage and track training experiments, automatically choose optimal hyperparameters, debug training jobs, and monitor the utilization of system resources such as GPUs, CPUs, and network bandwidth. We show how SageMaker Training tools enables faster distributed training, including libraries for data parallelism and model parallelism, and the Amazon SageMaker distributed training libraries automatically split models and training datasets across GPU instances to help you complete distributed training faster.

    Speaker: Gaurav Singh, Solutions Architect, AWS India
    Duration: 30mins


    Beyond model development and deployment with Amazon SageMaker model monitoring (Level 200)
    Unlike traditional software development, ML model development is an iterative process that requires the continuous monitoring of the deployed model’s input and output to ensure the optimum results. Join this session to learn the fundamentals of model monitoring with Amazon SageMaker. We cover how to detect the drift in your data and model, and share relevant steps to ensure your model quality in production.

    Speaker: Sahil Verma, Solutions Architect, AWS India
    Duration: 30mins


    Building text-to-image model with Amazon SageMaker (Level 200)
    Canva is an online platform for creating and editing content including presentations, social media posts, videos, documents, and websites. The company aims to democratize content creation so that all their users from enterprises to bloggers have the access to advanced visual communication tools. This session covers how Canva built their new text-to-image functionality with Stable Diffusion on Amazon SageMaker and launched it in less than 3 weeks. Learn how the end-to-end solution enabled Canva to remove heavy lifting from each step of the ML process, making it easier to develop high-quality models, rapidly roll out innovate new features to their users and scale up for future growth.

    Speakers: 
    Ben Friebe, Senior ISV Solutions Architect, AWS
    Greg Roodt, Head of Data Platforms, Canva

    Duration: 30mins


    Launch ML solutions at scale on AWS infrastructure (Level 200)
    AWS offers the broadest and deepest services around quickly building and launching AI and machine learning for all types of organizations, businesses, and industries. In this session, we explain how to deploy your inference models on AWS, explore 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: Santhosh Urukonda, Senior Prototyping Engineer, AWS India
    Duration: 30mins

  • Build, train, and deploy ML models track 2
  • Build, train, and deploy ML models track 2

    Build, train, and deploy ML models track 2

    About the track

    Learn how to build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows

    Operationalize your NLP workflows with AWS (Level 200)
    NLP models often consist of hundreds of millions of model parameters, thus building, training, and optimizing them requires time, resources, and skills. This session outlines how Amazon SageMaker helps you to quickly build and train large NLP models using popular frameworks such as PyTorch. We share the different distributed training and inference for large language models on Amazon SageMaker and explore how to operationalize your NLP pipeline.

    Speaker: Hariharan Suresh, Senior Solutions Architect, AWS
    Duration: 30mins


    Build, train, deploy, and operationalize Hugging Face models on Amazon SageMaker (Level 200)
    The field of natural language processing (NLP) is developing rapidly, and NLP models are growing increasingly large and complex. Through strong ecosystem partnerships with organizations like Hugging Face and advanced distributed training capabilities, Amazon SageMaker is one of the easiest platforms to quickly train NLP models. In this session, learn how to quickly train an NLP model from the Hugging Face transformers library with just a few lines of code using PyTorch or TensorFlow as well as SageMaker’s distributed training libraries.

    Speaker: Tapan Hoskeri, Principal Solutions Architect, AWS India
    Duration: 30mins


    End-to-end MLOps with Amazon SageMaker and Github Actions (Level 300)
    When you move your machine learning (ML) workloads into production, you need to look at creating automated model re-training and deployment pipelines. But building CI/CD around ML workflows and incorporating best practices such as source and version control, automatic triggers, and secure deployments can be challenging. In this session, we share how to operationalize and maintain your ML models in production efficiently with Amazon SageMaker Pipelines and bring CI/CD pipelines to ML, reducing the months of coding previously required to just a few hours. We demonstrate how to build and develop workflows by automating processes with third-party tools such as GitHub actions.

    Speakers: 
    Romina Sharifpour, Senior Solutions Architect, AWS
    Pooya Vahidi, Enterprise Solutions Architect, AWS

    Duration: 30mins

  • Data infrastructure for ML workloads
  • Data infrastructure for ML workloads

    Data infrastructure for ML workloads

    About the track

    Data drives today’s businesses and economies. Learn how to build a solid data infrastructure to help you deliver high performance AI and ML models trained by data. Harness the power of data to unlock insights and create new possibilities today.

    Scaling data processing and ML workloads with AWS (Level 200)
    Building scalable AI and machine workloads is a cross-team effort that requires management of several resources. The lack of proper management results in teams having to spend significant time on operational tasks, which slows down time-to-market, and keeping them from focusing on developing innovating products and solutions. In this session, we outline the options to scale complex AI/ML workloads on AWS. Learn how Amazon SageMaker Pipelines brings CI/CD pipelines to ML, reducing the months of coding previously required to just a few hours. Uncover options on how to deploy best-of-breed open source machine learning systems on AWS, enabling developers, data scientists and builders with the right tools to run machine learning on the cloud.

    Speaker: Vatsal Shah, Senior Solutions Architect, AWS India
    Duration: 30mins


    Improve your predictions with Amazon Aurora ML and Amazon SageMaker (Level 200)
    Today, majority of organizational data resides in relational databases, and the need to make this data accessible for training and using ML models to generate predictions in database-based applications has increased. This session demonstrates how to extract your production data from the relational database, build a ML model in Amazon SageMaker, and incorporate the model's findings into your production database and apps. We dive deep into how Amazon Aurora ML enables you to easily add ML-based predictions to applications via the familiar SQL programming language, without prior machine learning experience. Uncover how to build an optimized, and secure integration with AWS ML services without having to move data around.

    Speaker: Roneel Kumar, Senior Relational Databases Specialist Solutions Architect, AWS
    Duration: 30mins


    Breaking data silos with Amazon Redshift ML (Level 200)
    Data that you need for insights is not just growing in volume, but also getting more diverse. It often sits in various data silos, even third-party organizations Users are expected to work on transactionally consistent data but the process of transforming the data across these silos is fraught with issues like data duplication and loss, inconsistencies, inaccuracies, delays as data moves, coupled with network bottlenecks. In this session, we showcase how Amazon Redshift provides the deep integration into the AWS data ecosystem, across data lakes and purpose-built data stores, and deliver real-time and predictive insights you need without cumbersome data movement or data transformation.

    Speaker: Mary Law, Senior Manager Analytics, APJ Acceleration Lab, AWS
    Duration: 30mins


    Sustainable and scalable machine learning with Amazon EKS and Argo workflows (Level 200)
    Data science, machine learning, artificial intelligence, and Kubernetes have exploded in popularity in the last few years, resulting in organizations focusing on building out dedicated ML teams to help scale the delivery of ML-powered outcomes. As organizations scale the use of these technologies and practices, they face a number of challenges including the reproducibility of model outputs, reusability of pipelines, pipeline versioning, manageability of model deployment, and serving and automation of these end-to-end processes. In this session, we dive deep into how you can build a scalable architecture for ML data preparation, model training, and serving using Argo workflows and Amazon Elastic Kubernetes Services (Amazon EKS).

    Speaker: Mitch Beaumont, Principal Solutions Architect, AWS
    Duration: 30mins

  • ML for developers and builders
  • ML for developers and builders

    ML for developers and builders

    About the track

    At AWS, our goal is to put machine learning (ML) in the hands of every developer and data scientist. Learn and experiment how to use ML and transform the way we live our daily lives.

    Leveraging ML in your development workflow (Level 200)
    Developers and builders who are able to accelerate their workflow spend more time focusing on building innovative products and solutions. In this session, we explain how to speed up your entire development workflow when building applications on the cloud with AWS. Uncover how Amazon CodeWhisperer, provides you with ML-powered coding recommendations in your application development. We demonstrate the use of Amazon CodeGuru to provide best practice recommendations for your code, as well as security and performance analysis. We also cover how Amazon DevOps Guru helps you with your operations once your application is live.

    Speaker: Matt Coles, Principal Engineer, AWS
    Duration: 30mins


    Machine learning for developers: How to improve everyday tasks with AWS (Level 200)
    At AWS, we aim to put machine learning (ML) in the hands of all developers. In this session, learn the different ways AWS is empowering developers of all skill sets apply the tools and resources, which enable them to include AI features within their applications or build own custom ML models for specific use cases. The session provides a practical perspective with demos on what developers really need to know for their key tasks such as integration of ML in development, image processing, and handling tasks that require linguistic aspects.

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


    Bringing best software engineering practices to data science and machine learning (Level 300)
    In a world of MLOps and data science models in production, improving the reliability, design, and implementation of our machine learning code is top of mind for data scientists. Software engineering best practices such as test-driven development (TDD) can help achieve these goals; however, there is limited guidance on how to apply these practices to data science workflows. This session explores the what, why, and when of applying useful software engineering practices in a data science context, and covers practical solutions and designs to apply in the daily tasks.

    Speakers: 
    Josh Goyder, Senior Data Scientist, AWS
    Marcel Vonlanthen, Senior Data Scientist, AWS

    Duration: 30mins


    Scale your machine learning value creation from months to hours with AWS (Level 200)
    The ability to build systems to get insights such as sales forecasting, fraud detection, and demand forecasting is increasingly important for organizations dealing with data on a daily basis. Having this ability enables organizations to move faster by automating slow processes and embedding intelligence into their systems. Many users want to build and use prediction systems based on the data that they analyze and process every day, without having to learn about hundreds of algorithms, training parameters, evaluation metrics, and deployment best practices. This session covers how to use AWS no code/low code tools to run the common ML use cases; use a visual interface and start getting real value from their data quickly without writing a single piece of code or have any ML expertise.

    Speaker: Aman Sharma, Senior Solutions Architect, SMB APJ, AWS
    Duration: 30mins


    Democratizing data: Build no-code predictive dashboards with analytics and ML (Level 200)
    Access to all data for fast analytics at scale is key for 360-degree projects involving data engineers, developers, analysts, data scientists, BI professionals and other users. However, building such models requires depth of technical knowledge and resources. This session showcases the how to build and visualize accurate ML predictions in BI solutions. Learn how to prepare tabular datasets and train a ML model with Amazon SageMaker without writing a single line of code. We then demonstrate how Amazon QuickSight, a cloud-native, serverless, business intelligence with native ML integrations and usage-based pricing, allows users to meet varying analytic needs from the same source of truth through modern interactive dashboards, paginated reports, embedded analytics, and natural language queries.

    Speaker: Darshit Vora, Senior Startup Solutions Architect, AWS India
    Duration: 30mins


    Improving performance and availability of serverless applications for ML workloads (Level 200)
    With IT infrastructures consistently churning out record amounts of new data, ITOps are often under constant pressure to manage and analyze their workloads with traditional tools. New approaches are needed to help IT shift from reactive to proactive management incident resolution to boost application availability, save time to detect, resolve the most critical issues and, lower costs. In this session we cover how to apply AI and ML to proactively protect your applications from downtime.

    Speaker: Paul Kukiel, Enterprise Solutions Architect, AWS
    Duration: 30mins


    Accelerate your team’s machine learning journey with AWS DeepRacer (Level 200)
    Looking for an interesting and fun way to learn about Reinforcement Learning (RL), then look no further than AWS DeepRacer, where you can learn how to build ML models quickly. You can then experiment with different algorithms, neural network configurations, and simulate it on a virtual racetrack. Once you have built your ML model, you can race in the AWS DeepRacer League; the world’s first global autonomous racing league, open to anyone to compete for prizes and glory. Developers, start your engines today!

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

  • Builders Zone
  • Migrate to modernize your apps

    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.

    Mind controlled robot (Level 300)
    Brain Computer Interface (BCI) is a direct communication pathway to collect brain signals, interpret them and outputting commands to a connected device. Forward thinking organizations across industries are now looking at BCI to transform user experience, in various use cases including ensuring drivers’ safety by tracking cognitive load, monitoring fatigue inputs and using the data to recommend rest or enabling workers to interact with a connected device using robotic arm to navigate and operate it as an extension of their body, without juggling a controller with their hands. In this session, we demonstrate how Brain Computer Interface (BCI) device reads brainwaves and uses machine learning to translate them into real-time control signals for a robot. We share how this device, powered by Amazon SageMaker and AWS IoT classify the activities in the brain and accurately translating to action.

    Speakers:
    K V Sureshkumar, Prototyping Architect, AWS India
    Arun Balaji, Principal Prototyping Engineer, AWS India


    Forecasting energy usage with analytics and machine learning (Level 200)
    The increase in energy prices has financial implications for many organizations across many industries. In this session, we demonstrate how to generate highly accurate energy forecasts in a timely and affordable manner with analytics and machine learning, without requiring any prior ML experience. Organizations can proactively identify ways to predict pre-pay or month end energy usage, avoiding high energy bills that will impact operating costs, or use the forecast data to predict potential savings when applying different energy efficiency measures, as well as recommend the best measure to use.

    Speakers:
    Jeffrey Zeng, Associate Data Scientist, AWS
    Laine Wishat, Data Scientist, AWS


    Running a closed loop feedback visual quality inspection application at the edge (Level 200)
    Defect and anomaly detection in the quality inspection is a vital step to ensure the quality of the products, as timely detection of faults or defects and taking appropriate action have significant operational and quality-related costs. Many organizations are looking at ways to automate this process as manual inspection is time consuming, subjective and difficult to scale. In addition, manual feedback loop results production bottlenecks and slows down time to market. In this session, learn how to build a robust, effective, and scalable closed loop quality inspection at the edge, which enables objective decisions on visual inspection with a quick feedback loop and at low cost.

    Speaker: Derrick Choo, Solutions Architect, AWS


    Building ML applications for real-time drone video streams (Level 200)
    Drone data is becomingly increasingly important for many organizations due to its capabilities to collect information which cannot be accessed easily or operated quickly in scenarios such as ensuring quick deliveries during rush hour, property inspection, leak detection, stockpile volume calculation or digital surveying. In this session, learn how to analyze drone footage in real-time and unlock insights from your drone images with machine learning for better and faster decision making.

    Speaker: Ishan Joshi, Solutions Architect, AWS


    Detect social media fake news with graph machine learning (Level 200)
    Social media is commonly used today for sharing and consuming news but the spread of fake news can negatively impact the company brand, reducing customer confidence, and impacting revenues. This session showcases how to detect fake news based on the content and social context of the news on social media with machine learning on AWS. We demonstrate how Amazon Neptune ML, a machine learning technique purpose-built for graphs enables accurate predictions using graph data in hours instead of weeks, without the need to learn new tools and ML technologies.

    Speaker: Ganesh Sawhney, Solutions Architect, AWS India


    Building an audio classifier with Amazon SageMaker (Level 200)
    Audio classification has numerous applications in the field of AI such as chatbots, automated voice translators, virtual assistants, music genre identification, and text to speech applications. In this session, uncover how to build your own audio classifier using Amazon SageMaker. We demonstrate the end-to-end overview, from data ingestion to result modelling.

    Speakers: 
    Emma Arrigo, Associate Solutions Architect, AWS
    Anushree Umesh, Associate Solutions Architect, AWS


    Improving call center efficiency and omnichannel customer experience with an AWS QnA Bot (Level 200)
    Find out how to build an interactive and smart QnA bot. The AWS QnABot is an open source, multi-channel, multi-language conversational chatbot built on Amazon Lex that responds to your customer’s questions, answers, and feedback. Without programming, the AWS QnABot solution allows customers to quickly deploy self-service conversational AI on multiple channels including their contact centers, web sites, social media channels, SMS text messaging, or Amazon Alexa.

    Speakers: 
    Nieves Garcia, AI & ML BD lead, APJ Public Sector, AWS
    Melwin Pais, Senior Solutions Architect, AWS


    Build a real time air quality anomaly detector using AWS Lookout For Metrics (Level 300)
    The use of AI/ML to detect anomalies in the data involves a lot of complexity in ingesting, curating, and preparing data in the right format and then optimizing and maintaining the effectiveness of these ML models over long periods of time. In this session, we share how to build an automated air quality anomaly detector with Amazon Lookout for Metrics, Amazon Kinesis and Amazon Simple Notification Service (Amazon SNS). Learn how to manage the complexities in detecting anomalies, enabling organizations to quickly act on the data to reduce impact on business outcomes including employee productivity and consumer footfall.

    Speaker: Dhiraj Thakur, Senior Partner Solutions Architect, AWS India

  • Closing
  • Closing

    Closing

    Accelerate rapid innovation with data and AI/ML (Level 200)
    The most common value organizations are hoping to get from their data is smarter decision-making to create better products and services, transform customer experiences, improve operational efficiencies, and deliver business outcomes. This session provides a recap of the days' sessions and addresses some of the commonly asked questions related to data and AI/ML with AWS. Learn how AWS is helping organizations and builders in any industry remove undifferentiated heavy lifting of data management with automation and intelligence. Discover how the new developments in AWS AI/ML, demos of the new technologies provide insights on how to take advantage of untapped potential and innovate with confidence.

    Speakers:
    Dean Samuels, Chief Technologist, ASEAN, AWS
    Kris Howard, Head of Dev Relations, APJ, AWS

Session levels designed for you

INTRODUCTORY
Level 100

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

INTERMEDIATE
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.

ADVANCED
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.


Conference timings

  • Australia & New Zealand
  • Australia
     GMT+11 (AEDT)

    Timing 1: 10.30am - 4.00pm
    Timing 2: 4.30pm - 10.00pm

    New Zealand
     GMT+13 (NZDT)

    Timing 1: 12.30pm - 6.00pm
    Timing 2: 6.30pm - 12.00am

  • ASEAN
  • Singapore
    Malaysia
    Philippines
     GMT+8 (SGT/MYT/PHT)

    Timing 1: 7.30am - 1.00pm
    Timing 2: 1.30pm - 7.00pm
    Timing 3 Keynote rebroadcast: 
    8.00pm - 9.00pm

    Thailand
    Vietnam
    .
     GMT+7 (ICT)

    Timing 1: 6.30am - 12.00pm
    Timing 2: 12.30pm - 6.00pm
    Timing 3 Keynote rebroadcast:
    7.00pm - 8.00pm

    Indonesia
    .
    .
     GMT+7 (WIB)

    Timing 1: 06:30 - 12:00
    Timing 2: 12:30 - 18:00
    Timing 3 Keynote rebroadcast: 
    19:00 - 20:00

    Pakistan
    .
    .
     GMT+5 (PKT)

    Timing 1: 4.30am - 10.00am
    Timing 2: 10.30am - 4.00pm
    Timing 3 Keynote rebroadcast: 
    5.00pm - 6.00pm

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

    Timing 1: 5.00am - 10.30am
    Timing 2: 11.00am - 4.30pm
    Timing 3 Keynote rebroadcast: 5.30pm - 6.30pm

    Sri Lanka
     GMT+5.30 (SLST)

    Timing 1: 5.00am - 10.30am
    Timing 2: 11.00am - 4.30pm
    Timing 3 Keynote rebroadcast: 5.30pm - 6.30pm

  • Japan
  • Japan
     GMT+9 (JST)

    Timing 1: 8.30am - 2.00pm
    Timing 2: 2.30pm - 8.00pm
    Timing 3 Keynote rebroadcast: 9.00pm - 10.00pm

  • Korea
  • Korea
     GMT+9 (KST)

    Timing 1: 8.30am - 2.00pm
    Timing 2: 2.30pm - 8.00pm
    Timing 3 Keynote rebroadcast: 9.00pm - 10.00pm

Featured AWS speakers

Dean Samuels, Chief Technologist, ASEAN, AWS

Dean Samuels
Chief Technologist, ASEAN, AWS

.

Kris Howard, Head of Dev Relations, APJ, AWS

Kris Howard
Head of Dev Relations, APJ, AWS

.

Learn more about AI & machine learning on AWS

AWS named a Leader in IDC MarketScape for AI lifecycle software tools and platform APEJ 

AWS named a Leader in Gartner Magic Quadrant for Cloud AI Developer Services

100,000+ customers use AWS for machine learning

100,000+

customers use AWS for machine learning

10x increase in team productivity using Amazon SageMaker

10x

increase in team productivity using Amazon SageMaker

40% reduction in data labeling costs using Amazon SageMaker

40%

reduction in data labeling costs using Amazon SageMaker


Frequently Asked Questions

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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.

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Kristine has twenty years of experience helping companies build as a software engineer, business analyst, and team director. She is a frequent speaker at tech events and meetups including AWS Summits and TEDx Melbourne. Kristine is dedicated to meeting and working with developers across the region, and now heads up Developer Relations for AWS in APJ.

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Dean comes from an IT infrastructure background and has extensive experience in infrastructure virtualization and automation. He has been with AWS for the past ten 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.

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Hitesh Bhatia leads the Devops for Airtel Digital, and is managing the infrastructure for India’s biggest music app – WynkMusic, India’s premier OTT and Liveapp AirtelXStream. Hitesh has over 12 years of experience working with AWS and is a Certified Professional Solution Architect. He has extensive experience managing DevOps/SRE, with proven expertise in cloud computing DevOps/ SRE practices, continuous integration continuous deployments (CI/CD), monitoring, , Python, IaC (Terraform) and configuration management (Ansible). He has also created cost optimized architecture and best practice for FinOps.

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Donnie Prakoso has over than 17 years of experience in the technology industry, from telecommunications, banking to startups. In his current role as the Principal Developer Advocate at AWS covering ASEAN and AEM, Donnie specializes in containers, serverless computing, and microservices integration patterns and machine learning.

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Derek Bingham has over 18 years’ experience designing, architecting and building complex solutions across a range of industries including health, telecommunications, insurance, finance and defense. Derek has a special interest in cloud-native architecture, front-end, and mobile development. In his current role with AWS, Derek focuses on helping developers to build their applications on AWS. He is actively engaging technical audiences, communities, and user groups to share the latest AWS services and helping them build applications on AWS.

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Wendy Wong is an AWS She Builds Alumni and an AWS Data Community Builder based in Sydney, Australia with a Masters of Data Science degree and a Graduate Certificate in Editing and Publishing. Wendy has upskilled over 200 management consultants at the PwC Digital Academy in business analytics and is currently a Lead Instructor in Data Analytics at General Assembly Sydney. With over 7 years of experience in analytics and data science Wendy shares her knowledge through teaching and content creation on dev.to. Wendy is passionate about community. She was the Director of Women in Big Data Sydney and has organized the Women in Data Science Sydney conference with Stanford University. Wendy has also consulted for Qantas, Westpac, government, Lendlease, small business, startups and government agencies.

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Jones is a Developer Relations personal at Freshworks and an AWS Community Builder (Serverless). Living the journey from a full-stack developer to Cloud Architect for Serverless where he has not only evangelised Serverless with his fellow teammates but also helped customers to solution their needs with AWS Serverless tech stack. He has been active in AWS Community in India, ASEAN and Colomb. He also helps to evangelize Serverless in various UG Meetups, AWS Community Day, AWS Summit India and APAC Community Summit.

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Faizal is a tech entrepreneur, currently Founder & CEO at Ecomm.in and Xite Logic. Both are born-in-the-cloud startups that guide organizations with digital transformation into the AWS cloud which provide e-commerce platform solutions for community management and engagement platforms. Faizal is an ardent contributor to the AWS community. As an organizer of the AWS Hyderabad User Group, he helped organize AWS Hackathons, AWS meetups, re:Invent recaps, webinars, and AWS certification bootcamps. He is also a speaker at many of these events covering Networking, IoT, Storage, and Compute. His VPC Masterclass on YouTube has garnered over half a million views. He was a core organizing member and host for the AWS Community Day South Asia 2021 Online, which attracted over 24K viewers. He is also an active AWS Community Builder since 2020 and has built an AWS Q&A discussion forum for the community.

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Dipali is Vice President of Data Engineering at Natwest Group, with over 18 years of IT experience specializing in solution architecture and application modernization, and a focus on data-intensive applications. She is passionate about creating simple to implement and easy to maintain solutions for complex business problems. Dipali holds the AWS Solution Architect - Professional certification. Dipali is passionate about sharing her knowledge and experience with the community. She is also an AWS community Hero and LinkedIn Learning Instructor for AWS Cloud.

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Salvian works as a software engineer for the backend infrastructure team in Traveloka and is responsible for improving the productivity of the backend product development engineering teams. Specifically, he is also in-charge of modernizing the development process and platform (CI/CD) of the backend infrastructure teams.

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Chetan is the Vice President of Cloud Engineering at Biofourmis, with more than 18 years experience in building and managing enterprise product teams across the globe. He has built R&D teams of more than 60 engineers focusing on delivering secure, highly available SaaS solutions by forming the DevSecOps and customer engineering teams across product lines. He also has formed the Devops team to build the CI/CD pipeline and established best practices on agile project management across product lines.

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Ali is a software engineering leader living in Auckland, New Zealand focusing on solving real-world problems with technology. Ali has extensive experience in the software development lifecycle, focusing on building software using JS/TS and AWS services. Ali believes good software is built through collaboration. He also mentors and coaches developers and builders to learn and achieve sucess in their careers.

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Kristine has twenty years of experience helping companies build as a software engineer, business analyst, and team director. She is a frequent speaker at tech events and meetups including AWS Summits and TEDx Melbourne. Kristine is dedicated to meeting and working with developers across the region, and now heads up Developer Relations for AWS in APJ.