Explore enterprise MLOps through an in-depth interview with Simon Stiebellehner, lead ML Ops engineer at Transaction Monitoring Netherlands. Gain insights into the practical application of machine learning operations in anti-money laundering, the structure of ML Ops teams, and the role of centralized infrastructure in efficient model development. Learn about the challenges and strategies for implementing MLOps in a data-intensive financial services environment from an experienced professional in the field.
Enterprise MLOps: Insights from a Lead ML Engineer
Overview
Syllabus
[00:00.000 --＞ .260] Hey, three, two, one, there we go, we're live.
[00:02.260 --＞ .260] All right, so welcome Simon to Enterprise ML Ops interviews.
[00:09.760 --＞ .480] The goal of these interviews is to get people exposed
[00:13.480 --＞ .680] to real professionals who are doing work in ML Ops.
[00:17.680 --＞ .360] It's such a cutting edge field
[00:20.360 --＞ .760] that I think a lot of people are very curious about.
[00:22.760 --＞ .600] What is it?
[00:23.600 --＞ .960] You know, how do you do it?
[00:24.960 --＞ .760] And very honored to have Simon here.
[00:27.760 --＞ .200] And do you wanna introduce yourself
[00:29.200 --＞ .520] and maybe talk a little bit about your background?
[00:31.520 --＞ .360] Sure.
[00:32.360 --＞ .960] Yeah, thanks again for inviting me.
[00:34.960 --＞ .160] My name is Simon Stebelena or Simon.
[00:38.160 --＞ .440] I am originally from Austria,
[00:40.440 --＞ .120] but currently working in the Netherlands and Amsterdam
[00:43.120 --＞ .080] at Transaction Monitoring Netherlands.
[00:46.080 --＞ .780] Here I am the lead ML Ops engineer.
[00:49.840 --＞ .680] What are we doing at TML actually?
[00:51.680 --＞ .560] We are a data processing company actually.
[00:55.560 --＞ .320] We are owned by the five large banks of Netherlands.
[00:59.320 --＞ .080] And our purpose is kind of what the name says.
[01:02.080 --＞ .920] We are basically lifting specifically anti money laundering.
[01:05.920 --＞ .040] So anti money laundering models that run
[01:08.040 --＞ .440] on a personalized transactions of businesses
[01:11.440 --＞ .240] we get from these five banks
[01:13.240 --＞ .760] to detect unusual patterns on that transaction graph
[01:15.760 --＞ .000] that might indicate money laundering.
[01:19.000 --＞ .520] That's a natural what we do.
[01:20.520 --＞ .800] So as you can imagine,
[01:21.800 --＞ .160] we are really focused on building models
[01:24.160 --＞ .280] and obviously ML Ops is a big component there
[01:27.280 --＞ .920] because that is really the core of what you do.
[01:29.920 --＞ .680] You wanna do it efficiently and effectively as well.
[01:32.680 --＞ .760] In my role as lead ML Ops engineer,
[01:34.760 --＞ .880] I'm on the one hand the lead engineer
[01:36.880 --＞ .680] of the actual ML Ops platform team.
[01:38.680 --＞ .200] So this is actually a centralized team
[01:40.200 --＞ .680] that builds out lots of the infrastructure
[01:42.680 --＞ .320] that's needed to do modeling effectively and efficiently.
[01:47.320 --＞ .360] But also I am the craft lead
[01:50.360 --＞ .640] for the machine learning engineering craft.
[01:52.640 --＞ .120] These are actually in our case, the machine learning engineers,
[01:55.120 --＞ .360] the people working within the model development teams
[01:58.360 --＞ .360] and cross functional teams
[01:59.360 --＞ .680] ...
Taught by
Pragmatic AI Labs
