12-08, 21:00–21:30 (UTC), LLM Track
This talk explores a framework for how data scientists can deliver value with Generative AI: How can you embed LLMs and foundation models into your pre-existing software stack? How can you do so using Open Source Python? What changes about the production machine learning stack and what remains the same?
This talk explores a framework for how data scientists can deliver value with Generative AI: How can you embed LLMs and foundation models into your pre-existing software stack? How can you do so using Open Source Python? What changes about the production machine learning stack and what remains the same?
We motivate the concepts through generative AI examples in domains such as text-to-image (Stable Diffusion) and text-to-speech (Whisper) applications. Moreover, we’ll demonstrate how workflow orchestration provides a common scaffolding to ensure that your Generative AI and classical Machine Learning workflows alike are robust and ready to move safely into production systems.
This talk is aimed squarely at (data) scientists and ML engineers who want to focus on the science, data, and modeling, but want to be able to access all their infrastructural, platform, and software needs with ease!
No previous knowledge expected
Hugo Bowne-Anderson is Head of Developer Relations at Outerbounds. He is also the host of the industry podcast Vanishing Gradients. Hugo is a data scientist, educator, evangelist, content marketer, and data strategy consultant, with extensive experience at Coiled, a company that makes it simple for organizations to scale their data science seamlessly, and DataCamp, the online education platform for all things data. He also has experience teaching basic to advanced data science topics at institutions such as Yale University and Cold Spring Harbor Laboratory, conferences such as SciPy, PyCon, and ODSC and with organizations such as Data Carpentry.