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Operationalizing Machine Learning at Scale // Christopher Bergh // MLOps Meetup #64
Description
MLOps community meetup #64! Last Wednesday, we talked to Christopher Bergh, CEO, DataKitchen.
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// Abstract
Working on a technically difficult problem, there will be some things that are important no matter what industry you are in. Whether it's building cars in a factory, using agile or scrum methodology, or productionizing ML models, you need a few basics. Chris gives us some of his best practices in the conversation.
// Bio
Chris Bergh is the CEO and Head Chef at DataKitchen. Chris has more than 25 years of research, software engineering, data analytics, and executive management experience. At various points in his career, he has been a COO, CTO, VP, and Director of Engineering. Chris is a recognized expert on DataOps. He is the co-author of the "DataOps Cookbook” and the “DataOps Manifesto,” and a speaker on DataOps at many industry conferences.
// Takeaways
Your model is not an island. For success, Data science requires a high level of technical collaboration with other parts of the data organization.
// Related Links
On-Demand Webinar - Your Model is Not an Island: Operationalizing Machine Learning at Scale with ModelOps
https://info.datakitchen.io/watch-on-demand-webinar-operationalize-machine-learning-at-scale-with-modelops
----------- Connect With Us ✌️-------------
Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Chris on LinkedIn: https://www.linkedin.com/in/chrisbergh/
Timestamps:
[00:00] Introduction to Christopher Bergh
[02:57] MLOps community in partnership with MLOps World Conference
[04:34] Chris' Background
[07:59] "When we started with the company, I realized that the problem I have is generalizable to everyone. I'm getting enough there in years, and I wanted to remove the amount of pain that other people have."
[09:53] DataOps vs MLOps
[10:15] "I don't really honestly care what Ops you use, right? Hahaha! Call it your favorite Ops, 'cause first of all, as an engineer, I want precise definitions. I look at it from a completely odd-ball way, so you could call it whatever Ops term you want."
[12:45] Best practices of companies
[14:16] "When that code runs in production, monitor and check to see if it's right. Absorb it, monitor it, because the model could go out of tune. The data going into it could be wrong. The data transformation could break. Shit happens, and don't trust your data providers."
[19:00] The whole is still greater than its part
[20:26] "It is harder to focus on the results than just on a piece of the task. Don't spend