MLOps community meetup #3! Last Wednesday, we talked to Phil Winder, CEO, Winder Research.

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// Abstract
Phil Winder of Winder Research joined us for the 3rd installment of our MLOps community meetup. In this clip taken from the long conversation, he speaks about why or why not he sees companies automating the retraining of Machine Learning Models. You can find the whole conversation here: https://www.youtube.com/watch?v=MRES5IxVnME.

The topic of conversation for our virtual meetup was an in-depth look at a pyramid of software engineering best practices that built up to incorporate data science best practices. That is to say, we analyzed “the essentials”, "nice to have," and "optimal" ways of doing data science. 

Machine Learning/Data Science/AI is an extension of the technical stack. So you can't really talk about Data science best practices without accidentally talking about software engineering best practices. For example, model provenance doesn't count for anything if you don't have code or container provenance.  Just as Maslow has the basic human needs, so too do we have basic MLOps needs. Where does "MLOps", as a "thing", start and end? For example, the four very reasonable best practices of the operation of models, but these are usually consumed into higher-level abstractions because there is a lot more to do than "just" provenance.  

// Bio
Dr Phil Winder is a multidisciplinary software engineer and data scientist. As the CEO of Winder Research, a Cloud-Native data science consultancy, he helps startups and enterprises improve their data-based processes, platforms, and products. Phil specializes in implementing production-grade cloud-native machine learning and was an early champion of the MLOps movement.

More recently, Phil has authored a book on Reinforcement Learning (RL) (https://rl-book.com), which provides an in-depth introduction to industrial RL to engineers.  He has thrilled thousands of engineers with his data science training courses in public, private, and on the O’Reilly online learning platform. Phil’s courses focus on using data science in industry and cover a wide range of hot yet practical topics, from cleaning data to deep reinforcement learning.

He is a regular speaker and is active in the data science community.  Phil holds a PhD and M.Eng. in electronic engineering from the University of Hull and lives in Yorkshire, U.K., with his brewing equipment and family.

// This was a virtual fireside chat between Phil Winder and Demetrios Brinkmann. The relevant links can be found below:
Join our MLOps Slack community: https://go.mlops.community/slack  

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/  
Connect with Phil on LinkedIn: https://www.linkedin.com/in/drphilwinder/

Follow Phil on Twitter: https://twitter.com/DrPhilWinder 
Learn more about Phil's company, Winder Research: https://winderresearch.com/