MLOps.community How to Avoid Suffering in Mlops/Data Engineering Role // Igor Lushchyk // MLOps Meetup #55
MLOps community meetup #55! Last Wednesday, we talked to Igor Lushchyk, Data Engineer, Adyen.
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// Abstract:
Building Data Science and Machine Learning platforms at a scale-up. Having the main difficulty in finding the correct processes, and basically being a toddler who learns how to walk on a steep staircase. The transition from homegrown platforms to open source solutions, supporting old solutions and maturing them, makes data scientists happy.
// Bio:
Igor is a software engineer with more than 10 years of experience. With a background in bioinformatics, he even started a PhD but didn't finish it.
As a data engineer, Igor has been working for the last 6 or 7 years, or maybe more, because he was doing almost the same data engineering stuff, but his position was named differently.
Igor has been doing a lot of MLOps in 4-5 years now. He doesn't know what he was doing more than - Data Engineering or MLOps. And that’s how this topic came about.
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Timestamps:
[00:00] Introduction to Igor Lushchyk
[02:05] Igor's background in tech
[07:42] Tips you can pass on
[11:05] How these tools work, and how they play together, and what is underneath?
[13:18] Dedicated MLOps team
[13:55] Central Data Infrastructure Section
[16:57] Transfer over to open-source
[20:24] If you don't plan for production from the beginning, then it's going to be painful trying to go from POC to production.
[22:08] How do you handle data lineage?
[25:09] You chose that back in the day, but you're regretting it.
[26:34] "Try to use tools which solve 80% of your use cases, and maybe 20% you'll have the suffering, but at least it's not 100% suffering."
[27:27] Friction points
[28:53] Interaction with Data Scientists
[29:21] "We have alignment sessions. We have different levels of representation. We share our progress."
[32:42] Build verse by decisions
[34:04] When to build or grab an open-source tool
[35:51] Build your own or buy open-source?
[37:11] Certain maturity and a certain number of engineers
[38:11] Startup to go with open-source
[40:14] Correct transition process
[40:56] "There are no other ways but to communicate with data scientists. Your team needs to have a close loop for future priorities, what to take with you, and what to leave behind."
[44:51] What to use in the monitoring piece
[45:36] Prometheus and Grafana
[48:07] Do you have automatic retriggering monitoring of Models set up?
[51:55] Hardware for on-prim model training
[52:38] "Machine Learning model prediction is a spear bomb."
[53:55] War or horror stories
[54:15] "Guys, don't do context switching!"
[55:54] "I won't say that Adyen is a company that allows you to make mistakes, but you can make mistakes."