Developing functional and effective generative AI solutions requires addressing various challenges. Ensuring moderated content and factual accuracy without hallucinations, integrating proprietary and domain-specific knowledge, adhering to stringent data-residency and privacy requirements, and ensuring traceability and explainability of results all demand meticulous engineering efforts. In this hands-on workshop we will explore strategies to overcome these challenges, learn about best practices and implement examples using Cloud services.

Max. number of participants: 200
Laptop (with browser access) is required.

Target Audience: Data Architects, Data Engineers, Data Scientists, Machine Learning Engineers
Prerequisites: Basic knowledge about AI solutions and related Cloud services is a plus
Level: Advanced

Extended Abstract:
Generative AI is taking the world by storm and enterprises across industries are rallying to adopt the technology. However, developing functional and effective generative AI solutions within organizations requires addressing various challenges beyond the management of these novel machine learning models. Ensuring moderated content and factual accuracy without hallucinations, integrating proprietary and domain-specific knowledge, adhering to stringent data-residency and privacy requirements, and ensuring traceability and explainability of results all demand meticulous engineering efforts. Moreover, the user experience of the application has emerged as a crucial performance indicator, while maintaining a lean application footprint is essential for a positive business case.
In this hands-on workshop we will explore strategies to overcome these challenges and implement examples using Cloud services of Amazon Web Services (AWS). You'll get a temporary AWS account (free of charge) to participate but must bring your own laptop to participate. We will delve into best practices, design patterns, and reference architectures.