Hybrid Data Science Teams @SurveyMonkey

MLOps.community · Intermediate ·📐 ML Fundamentals ·6y ago

Key Takeaways

The video discusses the structure and collaboration of hybrid data science teams, specifically the roles and responsibilities of machine learning engineers and data scientists in managing model artifacts, versioning, and retraining, with a focus on the advantages of working together in tandem from the beginning of a project.

Full Transcript

JH has a question here about the line between the machine learning engineer and data scientists he's wondering creating managing model artifacts using repository for this model versioning retraining is this the role of ml engineer or a data scientist how do ml engineers and data scientists work together and when does the ml engineer take over or are they continuously working together because data scientists aren't often keen on software development or versioning yeah I know it's a great question I think each organization works differently so I can talk specifically from Survey Monkey is a few years ago it used to be like oh yeah I did a scientists do all the model development to give us the model artifact I'm gonna go like ok no here you go handoff that did not work work very well for us in the long run and since then we work together in tandem from the very beginning and that allows our data scientists to pick up on some software development it allows our ml engineers to understand the model at a very very low level so that makes sure that we can know from the get-go like oh actually you know we don't have this data available in our feature store real time like it it only gets populated once a day so like are you ok with that is that fine and our data scientists know this the requirements or limitations upfront or like oh yeah you know we can't support a 15 layer neural net we can only really support 10 layer ones at the moment so you know whatever you're looking to do we can't do that and because those conversations are happening earlier on our data scientists know what the restrictions are we know what the restrictions are and from there on we can really make sure that we don't have any I'm like blockers that we didn't foresee kind of appearing and as a result to be specific data scientists still are probably the leaves on the model development and that male engineers are more so the leads on the Purdue aspects but there's definitely some crossover and more and more crossover that it happens over time and I would say that over time I'd say I've gotten way more adept on the data science side of stuff and her data scientists have become way more adept also on the software engineering and you never know it's going along and if anything were to happen to our services I'm sure they'll be able to you know be able to chip in and make sure things are going smoothly

Original Description

MLOps Community Meetup #4 In the 4th online meetup for our MLOps.community We spoke with Shubhi Jain, Machine Learning Engineer and all-around great guy! In this Clip he talks about how data science teams are structured and why its advantageous. This is an excerpt taken from the longer conversation that can be found here: https://youtu.be/oq1g4s2dUHE Every organization is leveraging machine learning (ML) to provide increasing value to their customers and understand their business. You may have created models too. But, how do you scale this process now? In this case study, we looked at how to pinpoint inefficiencies in your ML data flow, how SurveyMonkey tackled this, and how to make your data more usable to accelerate ML model development. Shubhi Jain is a machine learning engineer at SurveyMonkey where he develops and implements machine learning systems for its products and teams. Occasionally, he’ll create YouTube videos about Machine Learning in collaboration with Springboard, an e-learning platform. He’s always excited to bring his expertise and passion for Data and AI systems to the rest of the industry. In his free time, Shubhi likes hiking with his dog and accelerating his hearing loss at live music shows. This was a virtual fireside chat between Shubhi Jain, Demetrios Brinkmann and the MLOps community. 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 Shubhi Jain on Linkedin: https://www.linkedin.com/in/shubhankarjain/
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The video teaches the importance of collaboration between machine learning engineers and data scientists in managing model artifacts and versioning, and how this collaboration can help identify and address potential blockers early on. It also highlights the advantages of working together in tandem from the beginning of a project. By watching this video, viewers can learn how to structure and manage hybrid data science teams effectively.

Key Takeaways
  1. Identify the roles and responsibilities of machine learning engineers and data scientists
  2. Determine the requirements for model development and deployment
  3. Set up a repository for model versioning and retraining
  4. Establish a collaboration process between machine learning engineers and data scientists
  5. Monitor and address potential blockers and limitations
💡 Collaboration between machine learning engineers and data scientists from the beginning of a project can help identify and address potential blockers early on, leading to more effective and efficient model development and deployment.

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