People story

People of Solita: Machine Learning Architect Jokke Ruokolainen

Jokke Ruokolainen Machine Learning Architect, Solita

Published 22 Dec 2022

Reading time 4 min

Meet our People of Solita. We have over 1500 employees in different locations with different skills and backgrounds – each with a story to share about what they do and what inspires them.

This is Jokke, our Machine Learning Architect based in Tampere.

A variety of work is what makes me thrive

I could describe myself as a Machine Learning Multitasker, as I tend to get involved with many different tasks and projects. At Solita, I’ve been able to expand my role and enjoy the wide variety of assignments, whether internal development projects or challenging cases with customers.

I’ve been working for Solita since August 2021, but I have years of experience in the industry. My interest in machine learning started when I studied data analytics and big data at the University of Kuopio. At the beginning of my tech career, I started with analytics and data visualization, and modeling. I also dived more into integrations.

My next quest was in the operative data scientist role, where I worked with very advanced data analytics solutions, including algorithms applied, for example, in online stores. That was when we started talking about Machine Learning Operations (MLOps), and I dived further into this area of learning and developing MLOps infrastructures.

In my previous workplace, I worked on a project where we tested automation for big machines like tractors and taught algorithms in a 3D world. That was a big thing; we even did a dissertation about it for the EU. It was also interesting for me because I was fixing and driving big forestry machines in my parents’ company in the early days of my career. So, in my case, I’ve had the opportunity to work with big machines from many different angles!

Internal development work, sales support, and customer projects

At Solita, I’ve been lucky to have the opportunity to work with a variety of things ranging from internal development to sales and all the way to customer projects. The internal work has involved, for example, creating cases for people to test and learn from new open-source solutions and cloud services. I also have my head around business development, pondering how we can expand our Machine Learning services and help our clients to solve business challenges with data.

I’ve regularly collaborated with offices in other Solita countries. Sweden has a big data science community, and we share learnings. I also like to coach colleagues and predispose them to new technologies and solutions. We have info sessions about new interesting things, and in the spring, I’ll organize a hackathon. The goal is to help us all to keep up to date on the developments in the industry.

I’m also involved in the projects, often specifically in the beginning, when we build the MLOps. Often my job is to be the technical lead and figure out how we can build the best solutions that fit the client’s context. In one case, for example, I was designing a proof-of-concept architecture in a set-up where we moved existing solutions to Google Cloud. That requires several things: data modeling, understanding of the required changes, taking into consideration the operative needs, and deciding which database we need on top of that.

Creating solutions that help make better data-driven decisions

Our machine learning solutions have a long-lasting impact when the work is done with competence and care. MLOps is a huge concept, so choosing the right technologies is important if we develop the platform from start to end. We must make it smooth to maintain and further develop the solutions after our work is done. But we also need to be aware of the culture; it’s a big change for the company culture if the customer is not used to automated decision-making.

We are in the middle of the next industrial revolution; in history, we’ve seen big leaps in wellbeing and income structure when this happened. These technologies are here to reduce manual work and increase efficiency. It means that people can use their time for more important and interesting things. Machine learning can detect causalities and do things that humans can’t. It results in better data-driven decisions.

Straightforward people with a nice drive

I’ve also enjoyed working with other Solitans. Cross-functional collaboration has taught me much about the surrounding things outside my own core know-how in ML infrastructure. I’ve always got help when needed; plenty of sparring buddies are around.

People here are open, friendly, and straightforward. The hybrid model works for me; these days, I tend to work from the office quite often. It’s nice to be surrounded by people with a nice drive, which is why I also enjoy office time.

We are constantly looking for new colleagues to join our caring community! Take a look at our open positions!