MLOps as Tool to Shape Team and Culture // Ciro Greco // MLOps Coffee Sessions #95
MLOps Coffee Sessions #95 with Ciro Greco, MLOps as Tool to Shape Team and Culture. // Abstract Good MLOps practices are a way to operationalize a more “vertical” practice and blur the boundaries between different stages of “production-ready”. Sometimes you have this idea that production-ready means global availability but with ML products that need to be constantly tested against real-world data, we believe production-ready should be a continuum and that the key person that drives that needs to be the data scientist or the ML engineer. // Bio Ciro Greco, VP of AI at Coveo. Ph.D. in Linguistics and Cognitive Neuroscience at Milano-Bicocca. Ciro worked as visiting scholar at MIT and as a post-doctoral fellow at Ghent University. In 2017, Ciro founded Tooso.ai, a San Francisco-based startup specializing in Information Retrieval and Natural Language Processing. Tooso was acquired by Coveo in 2019. Since then Ciro has been helping Coveo with DataOps and MLOps throughout the turbulent road to IPO. // MLOps Jobs board https://mlops.pallet.xyz/jobs // Related Links Company Website psicologia.unimib.it/03_persone/scheda_personale.php?personId=518 gist.ugent.be/members --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Ciro on LinkedIn: https://www.linkedin.com/in/cirogreco/en Timestamps: [00:00] Introduction to Ciro Greco [02:32] Ciro's bridge to Coveo [07:15] Coveo in a nutshell [11:30] Confronting disorganization and challenges [16:08] Fundamentals of use cases [18:09] Immutable data in the data warehouse [21:36] Data management in Coveo [24:48] Pain for advancement [29:56] Rational process and Stack [32:24] Habits of high-performing ML Engineers [35:46] Sharpening the sword [37:50] Attracting talents vs firing people [42:18] Wrap up