Building an MLOps Team? Key ideas to keep in mind

MLOps.community · Advanced ·🧠 Large Language Models ·6y ago

Key Takeaways

Building a data science team for MLOps capabilities requires diversity in expertise and experience, as well as considering different personality types such as jack-of-all-trades and experts in specific niches, utilizing tools like MLOps community meetup and Winder Research expertise

Full Transcript

on the team perspective though that that on the team front that that's a little bit more that's a bit more difficult because like we said that the stack keeps getting bigger and bigger and bigger over time the way companies generally solve that especially in bigger companies is by having teams full of people that have different expertise and you know that's that's a good strategy but I would kind of go a little bit further than just saying expertise it's important to have teams with different [Music] teams we've want more diversity I think it's trying to what I'm trying to say like a particular problem at the moment is that you know if you're white and male and speak English then that's that's pretty much the only the only person that you will meet when you meet those scientists and machine learning and and and that kind of limits a team's vision it limits the team's vision so so that diversity is really important as is as important as the diversity in experience but I think it is it is possible to find people that that do sort of naturally thrive in this slightly more fuzzy knowledge world but I mean it comes down to specific personal and she said it's not like the classic sort of personality split is like the difference between a jack-of-all-trades I'm not sure if that translates well it's a phrase that means them somebody that is is quite good a lot of things but not really an expert in one particular thing so I would consider myself as a bit of a jack-of-all-trades because I enjoy jumping around and learning about different new things so new new ideas and new things there are interesting to me they're not they're not something I worry about whereas other people are much more interested in the detail and you know focusing on a specific subject and they become a true expert in that very specific niche and I think there's I think the a room for both of those types of people but they're kind of suited towards slightly different things so somebody that's very detailed is probably more suited to a project that is a bit more longer-term and requires more focus in order to solve that problem we're a jack-of-all-trades type person would be more suited to something that's moving very fast in India and industry that's moving fast or a product this that's you know moving fast

Original Description

What are some key things to keep in mind when building a data science team to maximize MLOps capabilities? 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: 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", 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: https://www.linkedin.com/in/dpbrinkm/ Connect with Phil on LinkedIn: Follow Phil on Twitter: https://twitter.com/DrPhilWinder Learn more about Phil's company Winder research: https://winderresearch.com/
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1 Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
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 9 Automatically Retrain Machine Learning Models? Are best practices worth it?
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 ▶ Building an MLOps Team? Key ideas to keep in mind
Building an MLOps Team? Key ideas to keep in mind
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 11 Hierarchy of MLOps Needs
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 12 Bare necessities for getting an ML model into production
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 14 How Phil Winder got into Data Science and Software Engineering
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 15 Provenance and Reproducibility in Machine Learning; what is it and why you need it?
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 52 MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
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Building a data science team for MLOps requires considering diversity in expertise and experience, as well as different personality types, to maximize MLOps capabilities and achieve success in machine learning projects. This involves creating a team with a mix of jack-of-all-trades and experts in specific niches, and utilizing tools like MLOps community meetup and Winder Research expertise. By doing so, teams can improve their vision, adapt to changing industry needs, and deliver high-quality re

Key Takeaways
  1. Identify the need for diversity in expertise and experience in the data science team
  2. Consider different personality types, such as jack-of-all-trades and experts in specific niches
  3. Create a team with a mix of personality types and expertise
  4. Utilize tools like MLOps community meetup and Winder Research expertise
  5. Develop a project management strategy that leverages the strengths of each team member
  6. Implement MLOps workflows and design ML pipelines
💡 Diversity in expertise and experience, as well as considering different personality types, is crucial for building a successful data science team for MLOps

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