My team and I deployed our first machine learning model into production this year. There were some bumps on the way, but we managed to do it! In fact, I did not have any experience building data pipelines before I joined Ericsson in 2017. I started to build up this competence during my first year when we developed infrastructures to ingest and parse radio network data. This work has now enabled us to deliver continuous data analytics as well as ensuring good data quality. All this infrastructure experience paved the way for my team when we started to work on our automated machine learning solution. I believe that this end-to-end competence is what makes my team so great! In this talk I will go through my personal experiences as a data scientist at Ericsson.
Data scientist @ Ericsson
Ellinor Rånge is a junior machine learning engineer and data scientist at Ericsson. She will talk about the transition from studying machine learning at university to what it is working in a real data science team, with all the real-world complexities that entails: data engineering, monitoring, data quality, and devops. And, of course, about the machine learning models they try to get to production. Ellinor has also been active as a student ambassador, spreading the ML and data science gospel at the university.