AI Explained: From MLOps Theory to Practice
December 13, 2022
10 AM PT / 1 PM ET
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The biggest question facing data science and ML teams is how to strike the right mix of MLOps tools, culture, and practices to create enterprise-grade ML platforms suited for their needs.
Watch this webinar to learn:
- Comprehensive requirements for the end-to-end MLOps lifecycle
- Cultural practices implemented by the world’s leading ML teams
- How to prove ROI for your ML initiatives
AI Explained is our new AMA series featuring experts on the most pressing issues facing AI and ML teams.
Featured Speakers
Goku Mohandas
Founder
at
Made with ML
In the last 7 years, he has worked at Apple on the NLP & ML platform, Ciitizen [acquired] where he led the ML team in the oncology space, and Hotspot - his own startup in the rideshare space. Throughout his journey, he’s worked with brilliant engineering and product teams and learned how to responsibly develop, deploy and iterate on ML systems across various industries, stacks and scale. Goku currently works closely with teams from early-stage/F500 companies in helping them develop, deploy and maintain production ML applications while diving into the best and bespoke practices of this rapidly evolving space.
Krishnaram Kenthapadi
Chief AI Officer & Scientist
at
Fiddler AI
Prior to Fiddler, he was a Principal Scientist at Amazon AWS AI and LinkedIn AI, where he led the fairness, explainability, privacy, and model understanding initiatives. Krishnaram received his Ph.D. in Computer Science from Stanford University in 2006. He serves regularly on the program committees of KDD, WWW, WSDM, and related conferences. His work has been recognized through awards at NAACL, WWW, SODA, CIKM, ICML AutoML workshop, and Microsoft’s AI/ML conference (MLADS). He has published 50+ papers, with 4500+ citations and filed 150+ patents (70 granted).
