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[01:20.520 --＞ .800] So as you can imagine,
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Enterprise MLOps: Insights from a Lead ML Engineer
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- 1 [00:00.000 --＞ .260] Hey, three, two, one, there we go, we're live.
- 2 [00:02.260 --＞ .260] All right, so welcome Simon to Enterprise ML Ops interviews.
- 3 [00:09.760 --＞ .480] The goal of these interviews is to get people exposed
- 4 [00:13.480 --＞ .680] to real professionals who are doing work in ML Ops.
- 5 [00:17.680 --＞ .360] It's such a cutting edge field
- 6 [00:20.360 --＞ .760] that I think a lot of people are very curious about.
- 7 [00:22.760 --＞ .600] What is it?
- 8 [00:23.600 --＞ .960] You know, how do you do it?
- 9 [00:24.960 --＞ .760] And very honored to have Simon here.
- 10 [00:27.760 --＞ .200] And do you wanna introduce yourself
- 11 [00:29.200 --＞ .520] and maybe talk a little bit about your background?
- 12 [00:31.520 --＞ .360] Sure.
- 13 [00:32.360 --＞ .960] Yeah, thanks again for inviting me.
- 14 [00:34.960 --＞ .160] My name is Simon Stebelena or Simon.
- 15 [00:38.160 --＞ .440] I am originally from Austria,
- 16 [00:40.440 --＞ .120] but currently working in the Netherlands and Amsterdam
- 17 [00:43.120 --＞ .080] at Transaction Monitoring Netherlands.
- 18 [00:46.080 --＞ .780] Here I am the lead ML Ops engineer.
- 19 [00:49.840 --＞ .680] What are we doing at TML actually?
- 20 [00:51.680 --＞ .560] We are a data processing company actually.
- 21 [00:55.560 --＞ .320] We are owned by the five large banks of Netherlands.
- 22 [00:59.320 --＞ .080] And our purpose is kind of what the name says.
- 23 [01:02.080 --＞ .920] We are basically lifting specifically anti money laundering.
- 24 [01:05.920 --＞ .040] So anti money laundering models that run
- 25 [01:08.040 --＞ .440] on a personalized transactions of businesses
- 26 [01:11.440 --＞ .240] we get from these five banks
- 27 [01:13.240 --＞ .760] to detect unusual patterns on that transaction graph
- 28 [01:15.760 --＞ .000] that might indicate money laundering.
- 29 [01:19.000 --＞ .520] That's a natural what we do.
- 30 [01:20.520 --＞ .800] So as you can imagine,
- 31 [01:21.800 --＞ .160] we are really focused on building models
- 32 [01:24.160 --＞ .280] and obviously ML Ops is a big component there
- 33 [01:27.280 --＞ .920] because that is really the core of what you do.
- 34 [01:29.920 --＞ .680] You wanna do it efficiently and effectively as well.
- 35 [01:32.680 --＞ .760] In my role as lead ML Ops engineer,
- 36 [01:34.760 --＞ .880] I'm on the one hand the lead engineer
- 37 [01:36.880 --＞ .680] of the actual ML Ops platform team.
- 38 [01:38.680 --＞ .200] So this is actually a centralized team
- 39 [01:40.200 --＞ .680] that builds out lots of the infrastructure
- 40 [01:42.680 --＞ .320] that's needed to do modeling effectively and efficiently.
- 41 [01:47.320 --＞ .360] But also I am the craft lead
- 42 [01:50.360 --＞ .640] for the machine learning engineering craft.
- 43 [01:52.640 --＞ .120] These are actually in our case, the machine learning engineers,
- 44 [01:55.120 --＞ .360] the people working within the model development teams
- 45 [01:58.360 --＞ .360] and cross functional teams
- 46 [01:59.360 --＞ .680] ...
