MLOps: The New Role in Data Science Posted on : Jul 04 - 2022

The demand for consistent, reliable insights in-house has brought about a new role – the machine learning operations (MLOps) analyst. In this Q&A we learn about this role and what it can mean for companies and data science teams.

Machine learning operations (MLOps) analysts have burst onto the scene as demand has grown among businesses for consistent, reliable insights in-house. We speak to Monika Rzepecka, MLOps analyst at the market, consumer, and brand intelligence agency, GfK, to hear what the role involves and what it delivers, strategically.

What can the MLOps role involve?

MLOps is a very fresh area. It’s still developing, and companies have differing ideas on what the role is. Many businesses don’t even have it yet, but are considering it. In essence, MLOps analysts are a section of the data science team. Our role is to make machine learning, or AI, projects more systematic, repeatable, and well maintained. Whereas data scientists focus on algorithm design and the interpretation of information, MLOps concentrate on making sure the model is working at its best amid changing demands. We monitor algorithms metrics, improve performance, and set best practice. View More