Speakers
Alex Shang
Machine Learning
Snorkel AI
Passionate about the research and design of machine learning systems for challenging problems, especially those in healthcare. Half a decade of experience as a ML researcher and engineer, with authorship of key patents and publications behind FDA approved computer vision algorithms used by thousands of cardiologists for imaging in coronary interventions.
Currently building production AI systems for the world's largest enterprises at Snorkel AI. Please feel free to reach out if you'd like to learn more about Snorkel, we're a friendly bunch and we love working with passionate and intelligent people :)
André Balleyguier
Head of ML Field Engineering (EMEA)
Snorkel AI
Scaling out Snorkel AI across EMEA and the international markets, heading up the ML Field Engineering and Solutions team.
Snorkel AI proposes a highly differentiated data-centric AI platform, helping unlock the value of your unstructured data for your GenAI and ML projects.
Elena Boiarskaia
Head of Applied Machine Learning
Snorkel AI
Gabe Smith
Senior Machine Learning Success Manager
Snorkel AI
Throughout my career, I've sat at the intersection of utilizing data to develop cutting-edge AI/ML solutions for the Global 1000. At Snorkel, I help our customers be successful with our unique product, Snorkel Flow; so far, my consulting & ML experience has assisted in the scoping, development or delivery of over 60 AI/ML projects, helping drive cutting-edge AI/ML solutions from inception to production.
AI From the Trenches: Lessons Learned from Practitioners on the Front Lines
A moderated panel discussion featuring Snorkel machine learning engineers who’ve collaborated with some of the largest enterprises in the world to successfully build and deploy production AI/ML models.
The discussion will focus on the most common challenges faced by AI/ML engineers and data scientists, from expectation setting and use case prioritization to technical decisions and challenges. You’ll hear first hand about the lessons learned and best practices developed by Snorkel ML engineers, as well as recommendations for getting started, moving past PoCs and successfully delivering on the promise of AI into the enterprise.