Data and AI Virtual Summit

Digital transformation has become a broad, catch all term meaning anything from improving the customer experience to streamlining internal business processes and everything in between. Artificial Intelligence has also become a broad and ambiguous concept meaning everything from chatbots to self-driving cars. Now put those two concepts together and you end up with jargon soup, wasted resources, confusion, and failed initiatives. The industry is full of hype and confusion, buzzwords and bs, unrealistic expectations and some high visibility failures.

Whenever a significant technology change comes about, it’s difficult to not get caught up in the confusion and end up on costly paths without business value due to the fear of missing the next big thing and losing out to the competition

In this session we will hear from award winning author and popular conference speaker Seth Earley who will provide practical guidelines for getting real business value from Artificial Intelligence programs and projects and discuss what needs to be in place to be successful (hint: it’s not the technology).

Attendees will leave with a buzzword free understanding of:
- The necessary business decisions when considering these tools.
- Cultural issues and how to address them.
- How to approach processes to maximize business value

This L100  training provides a general introduction into GenAI as a technology, a run through of AWS GenAI services and an understanding of how these services can be applied to solve specific customer problems.

Companies in various sectors, including Social Media, e-commerce, gaming, and Media, are increasingly leveraging user-generated content to enhance engagement. Nevertheless, maintaining content that aligns with a company's policies and promotes a secure online community poses an escalating challenge. The introduction of AI-generated content (GenAI) adds a new dimension to this domain. In this session, we will present an AWS AI solution for content moderation with GenAI. The session will encompass both informative presentations and live demonstrations.

Data quality is foundational to success with AI. Most organizations venture into AI without assessing and investing in data quality leading to failed AI initiatives. How do you assess and improve data quality to ensure success with your AI applications? What tools and platforms are available for you to use and apply?

Generative AI is getting a lot of attention, headlines, and hype these days.  Every AI business is pivoting to generative AI. Organizations are beginning to use it for customer support and to unlock the potential of vast troves of unstructured data stored in systems that house knowledge assets

However, this emerging technology does not come with language models that reflect your organization’s specific lexicon, terminology, and vocabularies that describe content in a consistent and meaningful way.

Another challenge is that generative AI is not a retrieval mechanism that finds accurate, curated content. It creates new content based on patterns within the data. If it does not come up with an answer it will “hallucinate,” that is, make up an answer.

The success of generative AI largely depends on the effective management of the vast knowledge it can access and use effectively. In this webinar, we will explore the critical role of knowledge management in unlocking the full potential of generative AI.

We will begin by providing an overview of the fundamental concepts and advances in generative AI, followed by an in-depth examination of the importance of knowledge management in the development, implementation, and ongoing improvement of these systems.

We’ll discuss knowledge management approaches for organizing and retrieving information, and how retrieval fits in with content generation as well as the challenges and opportunities knowledge management presents for the enterprise.

Attendees will leave with practical takeaways for knowledge management strategies and practices that can support generative AI. Participants will learn how to optimize the performance of generative AI models by ensuring the appropriate handling of information and the integration of domain-specific expertise and specialized language models fine-tuned for each industry, organization, and process.

The first section of this session will review the different building blocks to develop custom LLM-powered apps. It will provide a tutorial to build an LLM-powered chatbot running on your data with Streamlit. The second section will show and demo how to use Snowpark Container Services to bring Large Language Models into your data. We will see how to run the Llama v2 model within Snowflake Data Cloud and use it to summarize support transcripts.

Live Q&A with:
- Seth Earley, Founder and CEO, Earley Information Science
- Lana Zhang, Sr. AI Services Solutions Architect, Amazon Web Services
- Gopal Krishna Bhatia, Sr. Partner Solutions Architect, Data & AI Specialist, Amazon Web Services
- Claudio Sousa, Managing Partner, Integrationworx
- Hunter Carlisle, Senior AI/ML Solutions Architect, Amazon Web Services
- Shibu Nair, Principal SA, Amazon Web Services
- Carlos Carrero, Architect AI/ML, System Integrators, Global, Snowflake

Aligning data, AI/ML, and engineering initiatives across the organization into a sustainable, cohesive whole requires cross-organizational coordination that is difficult to achieve, and must be built on top of a solid data foundation. In this talk, you will learn about the people and process considerations that should be taken into account as your organization builds a modern AI strategy: including assessing data-readiness for AI, the power of using targeted architectural spikes to orient strategic direction, empowering builders for innovation, and driving data literacy across the enterprise. We will share success stories from the thousands of AI/ML and data engagements that AWS has supported and provide actionable steps you can take to ensure your projects have maximum strategic impact.