Product teams are scrambling to figure out what they will be doing with this latest wave of AI technologies, and engineering organizations are struggling to bring generative AI into enterprise environments. Beyond anecdotal ChatGPT interactions and intriguing demos, teams need to know how to validate inconsistent model outputs, mitigate the risk of hallucinations, structure text completions, maintain data privacy, integrate private data, establish competitive advantages, plan development activities, and understand the landscape of tooling.
Leaving this workshop, you will be equipped with processes and knowledge to overcome each of these barriers, and you will have gained the practical, hands-on expertise to start integrating generative AI in your domain.
1 Learn the essential AI engineering skills of prompting, data augmentation, chaining, developing agents, validating/filtering model inputs and outputs, and fine-tuning.
2 Get hands-on with the latest generative AI models (i.e., Falcon, MPT, Stable Diffusion, Zeroscope, WizardCoder, etc.)
3 Gain a better understanding of how transformative AI applications are being architected via a new generative AI stack of tools/infra.
Founder & Data Scientist @Prediction Guard, Co-Host of the Practical AI podcast, Previously Built Data Teams at Two Startups and an International NGO
Daniel Whitenack (aka Data Dan) is a Ph.D. trained data scientist and founder of Prediction Guard. He has more than ten years of experience developing and deploying machine learning models at scale, and he has built data teams at two startups and an international NGO with 4000+ staff. Daniel co-hosts the Practical AI podcast, has spoken at conferences around the world (QCon, ODSC, Applied Machine Learning Days, O’Reilly AI, GopherCon, KubeCon, and more), and occasionally teaches data science/analytics at Purdue University.