Hi, everyone. Welcome to another episode of ThoughtWorks Technology podcast. I'm Jomack, one of your

co-host, and I'm here with my co-host, Mike. Hi, Mike. Hi, Jemak. I'm Mike Mason, who you

might also know from other podcasts. I'm one of the regular hosts here. We have a great episode for you.

We are going to talk about deploying and running machine learning machine learning

model in the wild.

I'm really excited to talk to two of our colleagues, Max Pagels and Jarno Cartela, from Finland. And Max, would you introduce yourself?

Yes, hi. Thanks for having me on. So my name is Max Pagels. I'm currently the head of technology

for a company called Fourkind, which has offices in Helsinki and Amsterdam. And Fourkind

is a recent ThoughtWorks acquisition.

So now I'm part of ThoughtWorks working from the Helsinki office with all things machine learning

and trying to figure out where technology might be in the future and helping everyone in that area.

Thanks, Max.

Jerno, would you say hi to the audience and introduce yourself?

Yes, hi, I'm Jarno.

I'm a principal consultant at Four Kind, and my day-to-day is trying to figure out how

organizations could be more intelligent and how to apply data science and data to their day-to-day work.

Glad to have you both here.

So the reason we thought it's a good idea to get together for this podcast was a conversation

we had in our last technology radar meetings.

There was a conversation that why machine deploying machine

learning model should be any different from deploying any other old microservices or executable

or application. What are the differences in kind of runtime configuration, runtime behavior,

...