Innovate Generative AI + Data logo
2:00 PM – 6:00 PM ― Thursday, March 13, 2025 | Hong Kong | Free Virtual Event

Built for breakthroughs

Join us at Innovate to discover how AWS can help you harness the full potential of generative AI and data.

Explore 7 learning tracks designed for you

From technical deep dives for builders to strategic sessions for business leaders, this event equips you with the inspiration and skills to navigate the world of generative AI and data on AWS.
  • Gen AI Journey

    Generative AI opportunities come from all across an organization. Line of business owners and job functions often surface the most salient business needs and use cases that could benefit from generative AI, and often partner with IT to evaluate, select, integrate, and implement new projects. This track will act as a guide for leaders from across organizations through each step of the generative AI journey, from idea inception through production. The sessions within this track will cover a wide array of topics throughout the process to help break down each step and how they should approach the decisions they will face as leaders.

    Crypto.com: Accelerating Content Creation with Gen AI on AWS - From Idea to Global Marketing in Minutes (Level 300)

    Hosting DeepSeek on EKS (Level 300)

    Driving Business Use Cases and Value at Scale with Generative AI (Level 100)

    Businesses across various industries are faced with the challenge of effectively leveraging generative AI to drive operational efficiency and enhance customer experiences. Identifying the most impactful use cases where generative AI can deliver substantial benefits is crucial. This talk explores how organizations can harness generative AI to develop innovative solutions that create value for their organizations.

    New York Life: Data Platform Modernization to Generative AI Innovation (Level 100)

    New York Life (NYL) modernized its on-premises data platform to enhance analytics, performance, and automation for its critical insurance operations. To meet these objectives, NYL built a scalable data lake and reporting platform on AWS using AWS Lambda, AWS Glue, Amazon RDS, and Amazon Redshift. In this session, NYL shares lessons learned from moving off its legacy platform to a modern data lake and how having a modern data foundation accelerated their generative AI journey. Learn how NYL is using Amazon SageMaker and Amazon Bedrock to improve employee productivity and front-line agent experience.

    The AWS Approach to Secure Generative AI (Level 200)

    At AWS, safeguarding the security and confidentiality of customers’ workloads is a top priority. AWS Artificial Intelligence (AI) infrastructure and services have built-in security and privacy features to give customers control over their data. Join this session to learn how AWS thinks about security across the three layers of our generative AI stack, from the bottom infrastructure layer to the middle layer, which provides easy access to all the models along with tools customers need to build and scale generative AI applications, and the top layer, which includes applications that leverage LLMs and other FMs to make work easier.

    7 Principles for Effective and Cost-efficient Generative AI Apps (Level 200)

    As generative AI gains traction, building effective and cost-efficient solutions is paramount. This session outlines seven guiding principles for building effective and cost-efficient generative AI applications. These principles can help businesses and developers harness generative AI's potential while optimizing resources. Establishing objectives, curating quality data, optimizing architectures, monitoring performance, upholding ethics, and iterating improvements are crucial. With these principles, organizations can develop impactful generative AI applications that drive responsible innovation. Join this session to hear from Mark as he provides actionable insights for your generative AI journey.

    Building a Mature Gen AI Platform Responsibly on AWS

    Most customers start on their GenAI journey with targeted POCs, realizing that they need to provision access to most capable models available on the market with an FM Hub and AI Gateway. As they progress to production and scale, they add components for data integration, orchestration of RAG or agents, observability, model customization, security, guardrails and governance, incorporating Responsible AI practices. In this talk we overview typical components of a mature GenAI platform, dive deeper into select components like gateway, orchestration, observability and explore how to build that responsibly on AWS.

  • Building and Scaling with Gen AI

    This track provides deep technical insights and practical guidance for building production-ready generative AI applications. Through detailed technical sessions, you'll explore advanced topics like multi-agent architectures, automated reasoning for responsible AI, efficient model selection, and scalable retrieval-augmented generation (RAG) implementations. Learn how to leverage Amazon Bedrock's comprehensive suite of tools and features to build secure, cost-effective, and high-performing AI solutions that drive real business value. This track will equip technical teams with the knowledge and best practices needed to successfully architect and deploy enterprise-grade generative AI applications.

    DeepSeek on AWS: Building a Secure AI Environment (Level 300)

    Agentic AI: The Next Evolution in Generative AI (Level 200)

    Scaling Generative AI Workloads with Efficient Model Choice (Phase 1) (Level 300)

    As customers build, deploy, and scale generative AI applications, using and managing the right set of models for the outcomes they desire becomes key. Amazon Bedrock is introducing several features designed to help customers find the right models, and help customers enhance cost-efficiency while maintaining world class performance and accuracy. Attend this session to learn about Amazon Bedrock Marketplace, Intelligent Prompt Routing, and Model Distillation.

    Amazon Nova Understanding Models

    Amazon Nova is a new generation of foundation models that deliver frontier intelligence and industry-leading price-performance. This session dives into Amazon Nova text and multimodal understanding models, their benchmark performances, and capabilities. Learn more about how these models excel in visual reasoning, agentic workflows, and Retrieval Augmented Generation (RAG). Experience video understanding on Amazon Bedrock and unparalleled customizability through text, image, and video input based fine-tuning and distillation. Join us to learn how Amazon Nova can transform your AI applications, from document analysis to API execution and UI actuation.

    Build Scalable RAG Applications Using Amazon Bedrock Knowledge Bases (Phase 1) (Level 300)

    Amazon Bedrock offers a managed Retrieval Augmented Generation (RAG) capability, connecting foundation models to your data. This session explores the latest Amazon Bedrock Knowledge Bases (KBs) techniques to improve response accuracy and optimize costs. Leverage Amazon Bedrock KBs' advanced chunking, parsing, and hallucination reducing capabilities for improved accuracy. Learn how to build scalable RAG solutions, delivering contextual responses while only paying for what you use.

    Leveraging Multiple Agents for Scalable Gen AI Applications (Level 300)

    Amazon Bedrock Agents handle tasks autonomously, streamlining operations for businesses. In this session, you'll learn how Amazon Bedrock Agents makes it easy to build agents and teams of agents on our secure, fully-managed service. We will demonstrate how you can build solutions that tackle multi-step tasks, automate existing APIs and databases, and easily integrate knowledge bases. See how agents can enable users to engage with support chat, access real-time answers, and automate actions across external platforms. Join Mark Roy to discover how coordinated AI agents, enhanced with guardrails to prevent misuse, are delivering the next generation of AI-driven customer engagement.

    Introduction to Automated Reasoning Checks in Amazon Bedrock Guardrails (Level 300)

    AWS launched Automated Reasoning (AR) checks in Amazon Bedrock Guardrails - making AWS the first major cloud provider to use automated reasoning that helps build transparent, responsible generative AI applications. Join us to learn about AR Check - a new Guardrails policy that uses sound mathematical techniques to reduce hallucinations, validate generative AI responses, and explain them in an auditable way. See how the Guardrails policy can help users generate more accurate LLM responses on highly regulated topics such as operational workflows, HR policies etc.; learn about the different use cases for AR checks; and how to get started today.

  • Using Gen AI in the Workplace

    Discover practical applications of generative AI tools that are transforming everyday workplace operations and productivity. Through comprehensive sessions, learn how Amazon Q's suite of solutions - including Q Business, Q Developer, and Q in QuickSight, can revolutionize enterprise workflows, software development, and business intelligence. This track offers insights into successful deployment strategies and best practices for maximizing the value of generative AI across your organization.

    Fast-Track Your AI & Data Initiatives with eCloudvalley: Amazon Q Consultancy and Managed Services Program (Level 200)

    TinTin Drugstore: Modernizing Pharmacy Experience with AWS - From IoT Intelligence to Seamless Product Discovery (Level 200)

    What's New with Amazon Q Business (Level 200)

    As enterprises grapple with fast technological change, join this session to learn about the latest product releases with Amazon Q Business. The session dives into the latest features and enhancements of Amazon Q Business, demonstrating how to deploy an Amazon Q Business application that leverages your enterprise content—empowering employees to answer questions, provide summaries, generate content, and securely complete tasks.

    A Fast and Easy Way to Build Applications with AWS App Studio (Level 200)

    Experience the future of enterprise app development with App Studio—a generative AI-powered service that uses natural language to create enterprise-grade applications, empowers technical professionals like IT project managers, data engineers, and enterprise architects to build highly secure, scalable, and performant business applications solving critical problems in minutes, without professional developer skills.

    Reimagine Business Intelligence with Generative AI (Level 200)

    In this session, get an overview of the generative AI capabilities of Amazon Q in QuickSight. Learn how analysts can build interactive dashboards rapidly, and discover how business users can use natural language to instantly create documents and presentations explaining data and extract insights beyond what’s available in dashboards with data Q&A and executive summaries. Hear from Availity on how 1.5 million active users are leveraging Amazon QuickSight to distill insights from dashboards instantly, and learn how they are using Amazon Q internally to increase efficiency across their business.

    Unleashing Generative AI with Amazon Q Developer (Level 200)

    Join us to discover how Amazon rolled out Amazon Q Developer to thousands of developers, trained them in prompt engineering, and measured its transformative impact on productivity. In this session, learn best practices for effectively adopting generative AI in your organization. Gain insights into training strategies, productivity metrics, and real-world use cases to empower your developers to harness the full potential of this game-changing technology. Don’t miss this opportunity to stay ahead of the curve and drive innovation within your team.

    Accelerate Multi-step SDLC Tasks with Amazon Q Developer Agents (Batch 2) (Level 100)

    While existing AI assistants focus on code generation with close human guidance, Amazon Q Developer has a unique capability called agents that can use reasoning and planning capabilities to perform multi-step tasks beyond code generation with minimal human intervention. Its agent for software development can solve complex tasks that go beyond code suggestions, such as building entire application features, refactoring code, or generating documentation. Join this session to discover new agent capabilities that help developers go from planning to getting new features in front of customers even faster.

  • Unified Experience for Your Data and AI

    In this track, you will learn how to empower your data personas with the next-generation Amazon SageMaker – your center for all data, analytics, and AI workloads. Discover how to break down data silos, optimize storage, and accelerate query performance with a unified experience that brings together AWS analytics and AI services. Master techniques for seamless data integration across sources with end to end governance of data and AI models built into the experience. You'll gain practical knowledge to build a future-ready analytics environment that enables faster analytics and AI development, reduces operational complexity, and drives more value from your data assets.

    Streaming Reimagined: The Latest Innovations in AWS Data Streaming (Level 300)

    Seemless Data Integration with Amazon Zero-ETL (Level 300)

    Build with Data and AI Faster in the next generation of Amazon SageMaker (Level 300)

    The rapid rise of generative AI is transforming how businesses approach data and analytics, blending traditional workflows and converging analytics and AI use cases. This session covers the next generation of Amazon SageMaker, the center for all your data, analytics, and AI, with a specific focus on SageMaker Unified Studio. Learn how Unified Studio brings together familiar tools from AWS analytics and AI/ML services for data processing, SQL analytics, machine learning model development, and generative AI application development into a single environment to enable collaboration and help teams build data products faster.

    Store Apache Iceberg Tabular Data at Scale with Amazon S3 Tables (Level 300)

    Amazon S3 Tables is purpose-built to store tabular data in Apache Iceberg tables. With Amazon S3 Tables, you can create tables and set up table-level permissions with just a few clicks in the Amazon S3 console. These tables are backed by storage specifically built for tabular data, resulting in higher transactions per second and better query throughput compared to unmanaged tables in storage. Join this session to learn how you can automate table management tasks such as compaction, snapshot management, and more with Amazon S3 to continuously optimize query performance and minimize cost.

    Accelerate Your Analytics and AI with Amazon SageMaker Lakehouse (Level 300)

    Data warehouses, data lakes, or both? Explore how Amazon SageMaker Lakehouse, a unified, open, and secure data lakehouse simplifies analytics and AI. This session unveils how SageMaker Lakehouse provides unified access to data across Amazon S3 data lakes, Amazon Redshift data warehouses, and third-party sources without altering your existing architecture. Learn how it breaks down data silos and opens your data estate with Apache Iceberg compatibility, offering flexibility to use preferred query engines and tools that accelerate your time to insights. Discover robust security features, including consistent fine-grained access controls, that help democratize data without compromises.

    Build and Optimize a Data Lake on Amazon S3 (Level 400)

    Organizations are building petabyte-scale data lakes on AWS to democratize access for thousands of end users. As customers design their data lake architecture for the right capabilities and performance, many are turning to open table formats (OTF) to improve the performance of their data lakes and to adopt enhanced capabilities, such as time-travel queries and concurrent updates. In this session, learn about recent innovations in AWS that make it easier to build, secure, and manage data lakes. Learn best practices to store, optimize, and use data lakes with industry-leading AWS, open source, and third-party analytics and ML tools.

    Streamline Data and AI Governance with Amazon SageMaker Catalog (Level 300)

    Discover how Amazon SageMaker Catalog, built on Amazon DataZone, transforms data and AI governance at scale. This advanced session explores three key capabilities: centralized artifact management, unified access control, and comprehensive lineage tracking. Learn to efficiently organize data and ML assets using semantic search with AI-generated metadata. We'll demonstrate implementing fine-grained permissions and setting up collaborative workflows. You'll also see how SageMaker Catalog enables automated data quality monitoring and sensitive data detection. Accelerate data analytics and model development, ensure compliance, and foster collaboration—ultimately driving faster time-to-market for your analytics and AI initiatives while maintaining robust governance.

  • Build and Train Foundation Models and LLMs

    Master the tools and techniques for building, training, and deploying large language models and foundation models at scale. Through detailed sessions, learn how Amazon SageMaker's comprehensive landscape enables efficient model development with features like HyperPod and optimized distributed training frameworks. Explore how AWS AI chips (Trainium and Inferentia) can help overcome computational challenges while reducing costs. This track demonstrates practical approaches to accelerate AI development using SageMaker Studio's integrated development environment, from initial experimentation to production deployment.

    DeepSeek Architecture & Optimization on AWS - A Complete Engineering Guide (Level 300)

    Efficiently Deploying DeepSeek on AWS - A Complete Guide for Developers, Machine Learning Engineer & Data Scientists (Level 300)

    Customize Foundation Models with Advanced Fine-tuning Techniques using Amazon SageMaker (Level 300)

    Amazon SageMaker AI allows data scientists and ML engineers to accelerate their generative AI journeys by deeply customizing publicly available foundation models (FMs) and deploying them into production applications. The journey begins with Amazon SageMaker JumpStart, an ML hub that provides access to hundreds of publicly available FMs, such as Llama 3, Falcon, and Mistral. Join this session to learn how you can evaluate FMs, select an FM, customize it with advanced techniques, and deploy it—all while implementing AI responsibility, simplifying access control, and enhancing transparency.

    Train Generative AI Models on Amazon SageMaker AI for Scale and Performance (Level 300)

    Amazon SageMaker AI offers the highest-performing ML infrastructure and a resilient training environment to help you train foundation models (FMs) for months without disruption. Top AI companies, from enterprises to startups, build cutting-edge models with billions of parameters on SageMaker AI. Discover how you can save up to 40% in training time and costs with state-of-the-art training capabilities such as Amazon SageMaker HyperPod, fully managed training jobs, and optimized distributed training frameworks. Join this session to learn how to run large-scale, cost-effective model training on SageMaker AI to accelerate generative AI development.

    Conquer AI Performance, Cost, and Scale with AWS AI Chips (Level 200)

    Generative AI promises to revolutionize industries, but its immense computational demands and escalating costs pose significant challenges. To overcome these hurdles, AWS designed purpose built AI chips, AWS Trainium and Inferentia. In this session, get a close look at the innovation across silicon, server, and datacenter and hear about how AWS customers built, deployed, and scaled foundation models across various products and services using AWS AI chips.

    Build, Train, and Deploy ML Models, Including FMs, for any Use Case (Level 300)

    Amazon SageMaker AI is a fully managed service that brings together a broad set of tools to enable high-performance, low-cost machine learning (ML) for any use case. With SageMaker AI, you can build, train, and deploy ML models, including foundation models (FMs) at scale using tools like notebooks, debuggers, profilers, pipelines, MLOps, and more – all in one integrated development environment (IDE). In this session, discover how you can get started along with the rest of the AWS ecosystem.

    Accelerate ML Workflows with Amazon SageMaker Studio (Level 300)

    Unlock the power of Amazon SageMaker Studio, a comprehensive IDE for streamlining the machine learning (ML) lifecycle. Explore data exploration, transformation, automated feature engineering with AutoML, and collaborative coding using integrated Jupyter Notebooks. Discover how SageMaker Studio and MLOps integration simplifies model deployment, monitoring, and governance. Through live demos and best practices, learn to leverage SageMaker Studio tools for efficient feature engineering, model development, collaboration, and data security.

  • Building a Data Foundation

    In this track, you will learn about foundational choices that provide flexibility for employing your data across any workload. You will gain practical skills for customizing and deploying generative AI applications using data from databases, file repositories, and your data lake. You will discover the power of metadata-driven data management with Amazon S3 Metadata and reimagine data streaming with end-to-end managed, and serverless capabilities. Finally, you will learn how Amazon DynamoDB was built to overcome the performance and scale limitations of relational databases, delivering consistent single-digit millisecond performance at any scale.

    FamilyMart: Enhancing Convenience Retail with Data Analytics on AWS - From Store Insights to Informed Decision Making

    The Master Blender’s Art - Balancing Extensibility, Scalability and Governance in Data Foundation for Enterprises (Level 300)

    A Practitioner’s Guide to Data for Generative AI (Level 300)

    In this session, gain the skills needed to deploy end-to-end generative AI applications using your most valuable data. While this session focuses on the Retrieval Augmented Generation (RAG) process, the concepts also apply to other methods of customizing generative AI applications. Discover best practice architectures using AWS database services like Amazon Aurora, Amazon OpenSearch Service, or Amazon MemoryDB along with data processing services like AWS Glue and streaming data services like Amazon Kinesis. Learn data lake, governance, and data quality concepts and how Amazon Bedrock Knowledge Bases, Amazon Bedrock Agents, and other features tie solution components together.

    Get Started with Amazon Aurora DSQL (Level 400)

    Amazon Aurora DSQL is a new relational database that combines the best of serverless experience, Amazon Aurora performance, and Amazon DynamoDB scale. Aurora DSQL's distributed architecture is designed to make it effortless for organizations of any size to manage distributed workloads with strong consistency. In this session, we guide you through the fundamentals of Aurora DSQL. Learn how Aurora DSQL can work within your architecture, understand key considerations and tradeoffs, explore what an application architecture could look like, and more.

    Unlock the power of your data with Amazon S3 Metadata (Level 300)

    Amazon S3 revolutionizes data discovery by automatically generating rich metadata for every object in your Amazon S3 buckets. Powered by Amazon S3 Tables, Amazon S3 Metadata provides a queryable metadata layer that allows you to curate, discover, and use your Amazon S3 data more efficiently. With Amazon S3 Metadata, you can explore and filter your objects based on attributes like object creation time and storage class to streamline data preparation for analytics, real-time inference, and more. Join this session to learn the power of metadata-driven data management with Amazon S3 Metadata.

    Build Streaming Data into Your Data Foundation (Level 300)

    Learn how AWS is reimagining data streaming with end-to-end managed and serverless capabilities across core infrastructure, systems operations, data integration, data processing, and data management for customers to modernize their data platforms. Learn about new and recent innovations for collecting, processing, and analyzing streaming data, including improved scalability, high resiliency, lower latency, and native integrations with many AWS and third-party services. Join this session to see how you can use AWS streaming solutions to build scalable, resilient data streaming applications for faster insights and improved decision-making.

    An Insider’s Look into Architecture Choices for Amazon DynamoDB (Level 400)

    To overcome the performance and scale limitations of relational databases, AWS built Amazon DynamoDB to deliver consistent single-digit millisecond performance at any scale for the most demanding applications on the planet. In this session, learn about the architecture choices for Amazon DynamoDB. Gain a better understanding of when to use DynamoDB and why DynamoDB is used by over one million AWS customers to power hundreds of applications that exceed half a million requests per second. Leave with a new perspective on how to design your own applications.

  • Bonus Track: Innovation Zone

    Exam Prep: AWS Certified Data Engineer – Associate

    Exam Prep: AWS Certified AI Practitioner

    Exam Prep: AWS Certified Machine Learning – Associate

Keynote speaker

Join Rahul Pathak, VP Data & AI/ML GTM at Amazon Web Services, for an inspiring exploration of AI's transformative potential across industries. This keynote will showcase how organizations are harnessing AI and data to drive innovation and maintain competitive advantage. Through compelling customer success stories and emerging trends, discover how companies are turning groundbreaking ideas into real-world innovations and learn why AI adoption is crucial for future business transformation.

Speaker image
Brian Wu

Brian Wu

AWS
Authorized Instructor Champion

Daniel Li

Daniel Li

AWS
Solutions Architect

Jason Kui

Jason Kui



 

Mark Roy

Mark Roy

AWS
Global Technical Lead, Amazon Bedrock
 

Mindy Ferguson

Mindy Ferguson

AWS
VP, AWS Messaging and Streaming
 

Terence Chow

Terence Chow



 

Carson Chan

Carson Chan



 

Haowen Huang

Haowen Huang



 

Jean Li

Jean Li



 

Mani Khanuja

Mani Khanuja

AWS
Technical Lead, Generative AI Specialists

Rex Law

Rex Law

AWS
Solutions Architect

Yanwei Cui

Yanwei Cui



 

Daniel Lee

Daniel Lee

AWS
Analytics Go-To-Market Specialist

Hemus Shum

Hemus Shum



 

Mark Relph

Mark Relph

AWS
Director, Data & AI Partners
 

Michelle Hong

Michelle Hong



 

Shawn Zhang

Shawn Zhang



 

eCloudvalley

Supporting Organizations

HKSTP
Kenfil
HKSTP
Kenfil

Session level guide

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.
BEGINNER
Level 100

Sessions cover best practices, service features, and detailed demonstrations. No prior knowledge of the topic is required.

INTERMEDIATE
Level 200

Sessions cover best practices, service features, and detailed demonstrations. Basic understanding of the topic is required.

ADVANCED
Level 300

Sessions provide in-depth technical coverage of selected topics. Some familiarity is expected, but hands-on experience is not required.

EXPERT
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.

Frequently asked questions