MLOps.community Operationalizing Machine Learning at Scale // Christopher Bergh // MLOps Meetup #64
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
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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 too much time doing the wrong thing."
[23:50] DevOps Principles and Agile
[27:17] DataOps Manifesto - DataOps is Data Management reborn
[27:45] "The 'Ops' term is ending up encompassing the work that you do in addition to the system you build to do the work."
[30:45] Standardization
[32:22] "I think that there's a lack of perception of the need to spend time on doing the operations part of the equation."
[34:15] Tools as Lego blocks
[34:49] "Good interphases make good neighbors."
[36:23] "Standards can help, but they're not the panacea."
[36:30] Cultural side - You build it, you own it, you ship it
[39:28] Value chain
[44:19] Ripple effect of testing
[48:03] Google on "One tool to rule them all"
[49:50] "Legacy happens if you're gonna live in the real world and not start greenfield projects."
[53:47] Starting MLOps in the legacy system