MLOps at a Reasonable Scale: Approaches & Challenges with Jacopo Tagliabue
May 11, 2022
Today, we’re joined by Jacopo Tagliabue, Director of A.I. at Coveo. He currently combines product thinking and research-like curiosity to build better data-driven systems at scale. They examine how immature data pipelines are impeding a substantial part of industry practitioners from profiting from the latest ML research.
People from super-advanced, hyperscale companies come up with the majority of ideas for machine learning best practices and tools, examples are Big Tech companies like Google, Uber, and Airbnb, with sophisticated ML infrastructure to handle their petabytes of data. However, 98% of businesses aren't using machine learning in production at hyperscale but rather on a smaller (reasonable) scale.
Jacopo discusses how businesses may get started with machine learning at a modest size. Most of these organizations are early adopters of machine learning, and with their good sized proprietary datasets they can also reap the benefits of ML without requiring all of the super-advanced hyper-real-time infrastructure.
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Previous guests include: Andy McMahon of NatWest Group, Jacopo Tagliabue of Coveo, Adam Sroka of Origami, Amber Roberts of Arize AI, Michal Tadeusiak of deepsense.ai, Danny Leybzon of WhyLabs, Kyle Morris of Banana ML, Federico Bianchi of Università Bocconi, Mateusz Opala of Brainly, Kuba Cieslik of tuul.ai, Adam Becker of Telepath.io and Fernando Rejon & Jakub Zavrel of Zeta Alpha Vector.
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