A Journey in Scaling AI // Gabriel Straub // MLOps Coffee Sessions #89

MLOps Coffee Sessions #89 with Gabriel Straub, A Journey in Scaling AI.  

// Abstract
Gabriel talks to us about the difficulties of scaling ML products across an organization. He speaks about differences in profiles of data consumers and data producers, and the challenges of educating engineers so they have greater insights into the effects that their changes to the system may have.

// Bio
Gabriel joined Ocado Technology in 2020 as Chief Data Officer, bringing over 10 years of experience in leading data science teams and helping organizations realize the value of their data. At Ocado Technology his role is to help the organization take advantage of data and machine learning so that we can best serve our retail partners and their customers.

Gabriel is a guest lecturer at London Business School and an Honorary Senior Research Associate at UCL. He has also advised start-ups and VCs on data and machine learning strategies. Before joining Ocado, Gabriel was previously Head of Data Science at the BBC, Data Director at notonthehighstreet.com, and Head of Data Science at Tesco.   

Gabriel has a MA in Mathematics from Cambridge and an MBA from London Business School.

// MLOps Jobs board  
https://mlops.pallet.xyz/jobs

// Related Links
Website: https://www.ocadogroup.com/about-us/ocado-technology
Podcast: https://www.reinfer.io/podcast/ai-pioneers-gabriel-straub-chief-data-scientist-ocado
Blog: https://www.ocadogroup.com/technology/blog

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Gabriel on LinkedIn: https://www.linkedin.com/in/gabriel-s-65081521/

Timestamps:
[00:00] Introduction to Gabriel Straub
[03:14] Best of Slack Newsletter
[04:06] Gabriel's best purchase since the pandemic
[05:37] Ocado's background and Gabriel's role
[07:55] Sliding scale of Ocada
[10:05] Different use cases of Ocada
[12:02] Realizing value with Machine Learning
[13:18] How things need to be computed on the edge
[14:51] Ocada's main day-to-day
[16:17] Being generalizable and when to stop
[19:11] The Golden Path
[21:30] Foundational level of maturity
[24:41] Metrics of success
[27:10] Lifespan of a data
[28:49] Hard lessons learned from producers and consumers
[30:19] Internal assessment
[32:50] Evolution of Ocado  
[36:58] Rule-based system
[38:58] Putting data science and/or machine learning value in front of the consumers
[41:55] Going past the constraints
[44:24] What holds people back?
[46:30] Instilling cultural value of doing right and well into the company
[49:42] Being defensive talking about AI
[51:44] Ocada is hiring!

Episode source