Abstract: The adoption and application of Large Language Models (LLMs) such as Llama 2, Falcon 40B, GPT-4, etc. in building generative AI applications is an exciting and emerging domain. This talk will dive into the end-to-end process of and framework for building a generative AI application, leveraging a fun and engaging case study with open-source tooling (e.g., HuggingFace models, Python, PyTorch, Jupyter Notebooks). We will guide attendees through key stages from model selection and training to deployment, while also addressing fine-tuning versus prompt-engineering for specific tasks, ensuring the quality of output, and mitigating risks. The discussion will explore the challenges encountered and emerging solutions and architectures developed. We aim to provide attendees with a pragmatic framework for assessing the opportunities and hurdles associated with LLM-based applications. This talk is suitable for AI researchers, developers, and anyone interested in understanding the practicalities of building generative AI applications.
Bio: Michelle is a technology leader that specializes in machine learning and cloud computing. She has 15 years of experience in the technology industry, contributed to the original IBM Watson showcased on Jeopardy, and enjoys building and leading teams that develop and deploy AI solutions to solve real-world problems. Michelle is passionate about diversity, STEM education/careers for our minority communities, and serves both on the board of Women in Data and as an avid volunteer for Girls Who Code.