Provenance and Reproducibility in Machine Learning; what is it and why you need it?
What does Machine Learning provenance mean and why do you need it? What are best practices in MLOps and is it worth it to implement them?
Phil Winder of Winder Research joined us for the 3rd installment of our MLOps community meetup. In this clip taken from the longer 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: • Hierarchy of Machine Learning Needs // Phi...
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", starts 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.
This was a virtual fireside chat between Phil Winder and Demetrios Brinkmann. relevant links can be found below.
Join our MLOps slack community: https://bit.ly/3aOTwgR
Connect with Demetrios on LinkedIn: / dpbrinkm
Connect with Phil on LinkedIn:
Follow Phil on Twitter: / drphilwinder
Learn more about Phil's company Winder research: https://winderresearch.com/