See the world through fresh AIs

Featured AWS Partner

• Track 1: The generative AI business 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.
  

  How Mixbook leveraged generative AI to offer personalized photo book experiences (Level 200)
  

  Mixbook creates personalized photo books and photo-related products for its customers. Mixbook wanted to enhance their customer experiences using generative AI, so they partnered with Mission Cloud to build a solution that would further personalize these unique photo books and products with both AI-driven recommendations and AI-generated images for backgrounds, stickers, and decorations.

  The solution combined Amazon SageMaker, Amazon Bedrock, and Amazon Rekognition to offer high-quality and original image suggestions based on user profiles. In this session, we explore how to leverage native ML services along with foundation models and Bedrock to create best-in-class customer experiences. We also discuss how to make appropriate recommendations that align with the customer's brand, all while allowing for innovative and diverse interactions.
  

  Generative AI use cases (Level 100)
  

  Generative AI represents an exciting new capability for all organizations, but the most impactful applications are those focused on tangible business value, not just technological novelty. In this session, explore an overview of the most impactful current use cases of generative AI across business functions and key industries. Learn best practices for generative AI implementation that uplifts processes, empowers people, and shifts mindsets to unlock demonstrable business value.
  

  From vision to value: Make data your differentiator for generative AI (Level 200)
  

  Generative AI is increasingly a point of focus globally. When done right, your data can be your differentiator in delivering generative AI applications that meet your specific business and customer needs. Leaders play a critical role in enabling this innovation by removing blockers, including siloed data, outdated governance, skilling, and legacy operating models. In this session, learn about strategies that help leaders maximize the value of generative AI by starting with the right data foundations and fostering a culture of innovation.
  

  Transform responsible AI from theory into practice (Level 100)

  The demand for responsible AI has increased globally. With an upswing in AI/ML technology use and regulation right around the corner, AWS has helped customers embrace the use of AI in a responsible manner—seeking to minimize risks, create a framework that maximizes business outcomes, and deliver fair systems. Building systems fairly requires a wide range of perspectives and expertise. In this session, learn about use cases that highlight the intersection of technology and society, and find out how the same approach can be used across various industries, including the public sector.
  

  Democratizing generative AI (Level 200)
  

  Learn how AWS is democratizing generative AI across the stack to make it easier for customers to leverage this game-changing technology and transform the way they engage with their customers and boost their employees’ productivity and creativity.
  

  Enhancing customer experiences with generative AI (Level 200)

  Generative AI presents new opportunities to enhance customer experiences and satisfaction. In this session, explore how businesses can leverage solutions like chatbots, virtual assistants, and Voice of the Customer analytics to enhance customer experience and engagement.
  

  Powering innovation across industries with generative AI (Level 100)

  Generative AI is sparking excitement across industries, promising to usher in a new era of technological progress. This session explores how leading companies in finance, healthcare, automotive, and more are using generative AI to create new revenue streams, improve efficiency, and deliver enhanced customer experiences. Join us as we examine the growing role of generative AI in reimagining businesses and customer relationships. We outline the opportunities and challenges of implementing generative AI, best practices, and the impact it has made on customer operations and offerings.
  

• Track 2: Build and scale with generative AI
  

  Building generative AI applications and models brings an abundance of new opportunities. This track will address the key decisions that builders will have to make and the tools available to make that process happen successfully and efficiently.

  From choosing the right model to fine tuning with your data, learn about the options to innovate quickly, easily, and securely with Amazon Bedrock.
  

  Customizing generative AI applications for your business using your data (Level 300)
  

  Customizing model responses enables you to deliver differentiated and personalized user experiences. In this session, learn about the different customization techniques offered by Amazon Bedrock, including model fine-tuning and continued pre-training as well as native support for end-to-end Retrieval-Augmented Generation (RAG) to deliver more relevant and accurate responses using your proprietary data.
  

  Keeping pets happy and healthy: Purina's journey with generative AI (Level 100)
  

  Nestlé Purina is harnessing generative AI to forge more personal connections with pet owners. Through their digital ecosystem of owned properties, Purina stays connected with pets and their owners throughout the lifetime of pet ownership. As they look to transition from transaction to driving longtime loyalty, Purina is exploring generative AI as an enabler within their digital ecosystem. When using generative AI to help guide pet owners to the best products and content to meet their unique needs, it’s critical Purina take necessary steps to retain the trust of the consumer. Purina is partnering with AWS to ensure responsible use of data for training, establish guardrails to help prevent model output that could potentially mislead the consumer, and ensure the consumer experience is delivered in Purina’s brand voice.
  

  Model choice in Amazon Bedrock (Level 100)
  

  Amazon Bedrock helps you rapidly adapt and take advantage of the latest generative AI innovations with easy access to a choice of high-performing FMs from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon. In this session, learn about how to experiment, evaluate, and build with the best FMs for your use case using Amazon Bedrock.
  

  Explore Amazon Titan foundation models in Amazon Bedrock (Level 300)
  

  Exclusive to Amazon Bedrock, the Amazon Titan family of models incorporates Amazon’s 25 years of experience innovating with AI and machine learning across its business. Amazon Titan foundation models (FMs) provide customers with a breadth of high-performing image, multimodal, and text model choices, via a fully managed API. In this session, learn how Amazon Titan models are created by AWS and pretrained on large datasets, making them powerful, general-purpose models built to support a variety of use cases, while also supporting the responsible use of AI. Use them as is or privately customize them with your own data.
  

  Central observability and governance for Amazon Bedrock (Level 200)
  

  Learn how to leverage Amazon CloudWatch to monitor Amazon Bedrock in near real-time. You can build customized dashboards for audit purposes and track usage metrics to understand model invocations and token count across all your foundation models and accounts. Bedrock also offers model invocation logging, a feature that can be used to collect metadata, requests, and responses for all model invocations.

  Discover how AWS Audit Manager can help ensure that your use of Amazon Bedrock follows AWS generative AI best practices for compliance and governance. Audit Manager assists in continuously auditing your generative AI usage, automating audit evidence collection, and providing audit ready reports to handle your compliance and audit needs. Audit Manager's generative AI controls can especially assist with tracking model usage permissions, flagging sensitive data, and alerting on issues.
  

  Simplify generative AI app development with Agents for Amazon Bedrock (Level 300)

  Foundation models (FMs) effectively conduct conversations, create content, and drive productivity, but they can deliver more value if they’re equipped to transact with company systems to complete multistep tasks. Fully managed Agents for Amazon Bedrock enables generative AI applications to help perform multistep tasks using enterprise systems and data sources, saving you from engineering prompts, managing session context, or manually connecting systems. In this session, dive deep into how to build agents using Amazon Bedrock.
  

  Build generative AI apps responsibly using Guardrails for Amazon Bedrock (Level 300)
  

  Guardrails for Amazon Bedrock help organizations manage end-user experiences based on their application-specific requirements and responsible AI policies. You can deliver consistently safe user experiences through generative AI applications, no matter the underlying FM. Learn how this new capability gives you the ability to define custom policies and manage interactions between users and FMs by filtering out disallowed topics and harmful content. Finally, observe demos on how to create and apply custom tailored guardrails with FMs and Agents for Amazon Bedrock to implement responsible AI policies within your generative AI applications.
  

• Track 3: Boost employee productivity and creativity
  

  Generative AI will have a profound impact on how work gets done. This track will show how organizations across industries can use generative AI applications to improve employees’ productivity and creativity.
  

  Generative AI in the workplace (Level 200)
  

  Employees have creative potential waiting to be unlocked. Generative AI holds the key. With generative AI-powered assistants we can transform the way we work. Using natural language, employees can generate code, create marketing content, draft emails and documents, discover and summarize knowledge, or transform data into visuals and stories that will simplify decision making. Join this session to learn how Amazon Q is helping organizations boost workplace productivity, creativity, and efficiency across every function.
  

  Boost productivity and creativity with AWS generative AI (Level 200)
  

  Marketing and sales professionals face ever-growing challenges capturing consumer and customer attention in a highly competitive landscape. Organizations implement generative AI to automate repetitive tasks, freeing time to create more engaging narratives and meet more customers. This session explores generative AI’s transformative creativity augmentation and productivity-boosting capabilities for marketing and sales teams. See how generative AI helps marketing teams generate creative content and helps sales teams automate and personalize communications and enablement assets with Amazon Q and Amazon Bedrock.
  

  Developing the workforce of the future with generative AI (Level 100)
  

  Join this session to see how startups have leveraged generative AI to develop products for the workforce of the future. Learn how these startups are democratizing creativity, boosting productivity, and shaping the new economy.
  

  Amazon Q, your business expert (Level 200)
  

  In this session, learn how Amazon Q in QuickSight enhances business productivity using Generative BI capabilities to turn insights into impact faster. Discover the simplicity of the new dashboard authoring capabilities in Amazon Q that allows business analysts to use natural language prompts to build visuals and calculations with ease and refine visuals instantly. Amazon Q furthers data democratization with data stories, helping users generate interactive, customizable narratives using natural language prompts. Learn how Amazon Q furthers data exploration for business users with LLM-powered executive summaries and a new context-aware data Q&A experience.
  

  Transform every agent into your best agent with Amazon Q in Connect (Level 200)

  Contact center agents are the first, and sometimes only, human interaction your end customers have. However, like all employees, these agents have a range of experience and abilities, impacting your customers’ relationship to and satisfaction with your company. While training, certifications, and simulations all help improve agents’ capabilities, they require a team to manage and are limited to non-live customer interactions. Instead, learn how generative AI can help new agents begin taking live contacts quickly and with proficiency, as well as accompany all agents on their most challenging interactions.
  

  Amazon Q in QuickSight (Level 200)
  

  In this session, learn how Amazon Q in QuickSight enhances business productivity using Generative BI capabilities to turn insights into impact faster. Discover the simplicity of the new dashboard authoring capabilities in Amazon Q that allows business analysts to use natural language prompts to build visuals and calculations with ease and refine visuals instantly. Amazon Q furthers data democratization with Data stories helping users generate interactive, customizable narratives using natural language prompts. Learn how Amazon Q furthers data exploration for business users with LLM powered executive summaries and a new context-aware data Q&A experience.
  

  Use foundation models to solve business challenges with SageMaker Canvas (Level 200)
  

  Amazon SageMaker Canvas helps business analysts build their own models without writing a single line of code. In this talk learn how to access and evaluate foundation models for top business needs such as content generation, text extraction, and text summarization from a visual, no-code interface. We explore how you can get started with no-code tools for each step of the ML lifecycle including data preparation and ready-to-use models that can be customized with your own company data.
  

• Track 4: Build a data foundation 
  

  When you want to build generative AI applications that are unique to your business needs, your organization’s data is your differentiator. Data is the difference between a general generative AI application and one that truly knows your business and your customer.

  Whether you are building your own model or customizing a foundation model, you need a data strategy that ensures you have relevant, high-quality data. In this track, learn how to build a data foundation to fuel your generative AI strategy.
  

  How Rocket Companies modernized their data strategy on AWS (Level 200)
  

  Rocket Companies power 10 million automated AI decisions per day to deliver fast, personalized customer experiences. Join this session to learn how Rocket Companies leverage Amazon SageMaker Studio and Amazon EMR to improve scalability and reduce the administrative burden of their data science operations. See how they apply services such as Amazon Athena, AWS Lake Formation, and Amazon EKS to enhance data accessibility, analysis, and integration. Learn how data ingestion workloads that once took days can now run in a matter of hours and deployments can run in days, not weeks, helping their data scientists iterate and innovate faster.
  

  Data patterns for generative AI applications (Level 200)
  

  Generative AI has captured imaginations and is transforming industries. However, to best take advantage of generative AI, you need an effective data strategy. Foundation models and other large language models need data to deliver a domain-specific experience, whether for in-context learning, fine-tuning, or training purposes. In this session, learn how to formulate an effective data strategy to build generative AI applications and accelerate your efforts using data-centric architectural patterns.
  

  Integrate your data lake into a generative AI workflow (Level 200)
  

  How can you turn your existing data lakes into a business advantage with generative AI? In this session, learn how to take control of the massive amount of unstructured, semi-structured, and structured data within your data lakes to accelerate pre-training, fine-tuning models, and implement retrieval augmented generation (RAG). You won’t want to miss this session if you want to understand the top data considerations when integrating your data lake into the generative AI workflow and get a deep dive into real-world customers who put existing datasets to work for generative AI-powered applications.
  

  End-to-end data and machine learning governance on AWS (Level 200)
  

  A data governance framework empowers organizations to protect, curate, and understand their data. In this session, learn how to structure a data governance program in direct support of funded business initiatives. Learn about the people, process, and technology capabilities necessary to deliver results, including unique considerations for a generative AI strategy.
  

  Best practices for querying vector data for generative AI apps (Level 400)
  

  PostgreSQL makes it easier to store and query vector data for AI/ML use cases with the pgvector extension. Learning best practices for vector search will help you deliver a high-performance experience to your customers. In this session, learn how to store data from Amazon Bedrock in an Amazon Aurora PostgreSQL and learn what SQL queries and tuning parameters optimize the performance of your application when working with AI/ML data, vector data types, exact and approximate nearest neighbor search algorithms, and vector-optimized indexing.
  

  Integrate data faster and easier with a zero-ETL future (Level 200)
  

  The most impactful, data-driven insights come from getting a full picture of your business and customers. This can only be achieved when you connect the dots between your different data sources. With data spread across multiple departments, services, databases, and third-party applications, you need to be able to easily connect to data across silos to get the best insights. Typically, connecting data across different data silos requires complex extract, transform, and load (ETL) pipelines, which can take hours, if not days. That’s just not fast enough to keep up with the speed of decision making. Come learn how AWS is investing in a zero-ETL future so you can quickly and easily connect to and act on all your data across ingestion, analytics, machine learning, and business intelligence as well as third party data. With direct integrations between AWS services, we are eliminating ETL for common use cases so teams can move faster.
  

  How to become a data-driven public sector organization (Level 100)
  

  Public sector organizations are consistently striving to improve their operational efficiency, enhance services, and optimize the constituent and student experience. Establishing an effective enterprise data program helps connect business processes to mission outcomes. In this session, learn critical components of a public sector data program, why you need them to be successful, and how they work together. Ultimately, we uncover the use cases that will help your organization become data-driven.
  

• Track 5: Create predictive and foundation models
  

  Building, training, and deploying foundation models requires purpose-built tools, infrastructure and workflows. This track covers the latest innovations from AWS to meet the unique storage, infrastructure, and developer tool requirements of foundation models with examples of how top model providers like AI21 Labs, Technology Innovation Institute, and Hugging Face build on AWS.
  

  Foundation models: Blueprints for success (Level 300)
  

  This unique session offers a deep dive into the intricacies of constructing foundation models from the ground up. The talk unravels the layers of complexity in building robust, scalable models, emphasizing the pivotal role of Amazon SageMaker and AWS silicon and infrastructure, to drive world-class accuracy and efficiency. Gain insights from the team that supports the world's leading foundation model providers and their best practices harnessing the power of AWS’s advanced tools to leapfrog competition.
  

  Train and tune your own foundation model (Level 300)
  

  Train foundation models faster on Amazon SageMaker with state-of-the-art training tools and the highest-performing ML compute infrastructure currently available. SageMaker interactive debugging and profiling tools can uncover complex model behaviors and dissect hardware utilization in near real time. You can also optimize distributed training jobs with the fastest and easiest methods for training foundation models and datasets. Join this session to get prescriptive guidance across the entire journey of foundation model customization and pretraining to help you accelerate generative AI development.
  

  Democratizing mental health screening with Aiberry and Amazon SageMaker (Level 200)
  

  Depression is a widespread, persistent, and burdensome illness. Due to limited access to screening, it frequently goes undiagnosed. Artificial intelligence models that analyze spoken responses to a brief guided conversation with a virtual assistant may provide an effective, efficient alternative to traditional screening methods. Aiberry is an innovative mental health assessment platform utilizing predictive AI to examine user text, audio, and video during a brief conversation. It instantly delivers a quantified risk score and symptom insights. This session demonstrates how Aiberry leverages Amazon SageMaker and other key services to make reliable mental health evaluations widely accessible. We discuss the technology behind parsing audio cues with natural language processing, training machine learning algorithms on clinical data, and deploying scalable inferences. Attendees gain insight into how AI is transforming mental healthcare through examples of real-world impact.
  

  A deep dive on AWS infrastructure powering the generative AI boom (Level 200)
  

  To help you realize the promise of generative AI, AWS provides highly performant, scalable, and cost-effective machine learning infrastructure. From the latest GPUs to purpose-built accelerators AWS Trainium and AWS Inferentia, from 3,200 Gbps of Elastic Fabric Adapter networking to hundreds of Gbps of data throughput with Amazon FSx for Lustre, AWS is the best place to train and deploy models in the cloud. In this session, get a close look at the innovation across AWS infrastructure and hear about how AWS customers built, deployed, and scaled large text and image generation models across various products and services.
  

  Accelerate foundation model development with Amazon SageMaker (Level 300)
  

  Amazon SageMaker provides managed infrastructure and tools to accelerate scalable, reliable, and secure foundation models (FMs) development. In this session, we dive into how Amazon SageMaker makes it easy to access FMs—including the best-performing, publicly available FMs. Then, we show you how to compare and select the right FM for your use case in minutes, followed by how you can analyze, evaluate, test, retrain, and deploy FMs using Amazon SageMaker, enabling you to quickly get started with generative AI—all in Amazon SageMaker Studio.
  

  Deploy FMs on Amazon SageMaker for price performance (Level 300)
  

  As you advance in your journey from evaluating foundation models (FMs) to building generative AI applications at scale, you need services to deploy these models at the best price performance. From low latency (a few milliseconds) and high throughput (millions of transactions per second) use cases for chatbots to long-running inference use cases for natural language processing, you can use Amazon SageMaker for virtually all your inference needs. In this session, take a deep-dive tour of features that make SageMaker a great choice for deploying FMs for inference and learn how you can benefit from AWS innovations.
  

  FMOps/LLMOps for FMs with Amazon SageMaker and Amazon Bedrock (Level 300)
  

  Creating reliable and repeatable workflows for generative AI applications requires additional tooling and practices that build on traditional MLOps. In this session, get a comprehensive overview of lifecycle management for foundation models from Amazon SageMaker JumpStart and Amazon Bedrock that power your generative AI applications. Dive deep into operational efficiencies across the lifecycle, including model selection and customization, model evaluation, model deployment, augmentation workflows, monitoring and traceability, and ongoing management. Explore multiple Amazon SageMaker MLOps services (such as SageMaker Experiments, SageMaker Pipelines, SageMaker Model Registry, and Model Monitor) for the reference architectures presented in this session.