MLOps teamwork lead
Simon Case
Simon Case Head of Data

Tech Focus Wed 6th July, 2022

Planning for Success: Suggested MLOps Team Structure

Data scientists play a vital role in operationalising machine learning. Within MLOps, data scientists are responsible for developing, evaluating and amending models as they move into production.

But what happens when it comes to software or platform knowledge, and integrating models into business systems?  


A successful MLOps team absolutely needs data scientists – but that isn’t all. If you want to deploy ML models successfully, you’ll need  a cross-functional team of experts who provide a number of important skills. An ideal MLOps team structure should include:  

  • Data scientists to create, build and amend the model
  • Platform or machine learning engineers to provide an environment to host the model
  • Data engineers to create the production data pipelines to retrain the model
  • Software engineers to integrate the model into business systems

It’s worth noting that in some smaller organisations, these roles might be part-time and performed by one person, or one person might fulfil more than one role. In larger organisations, there could be multiple people providing each function.

No matter the size of your team, it’s important that everyone has an idea of the responsibilities and requirements of other team members and roles. As your model moves from prototype to production, it’s important everyone understands the concerns of other team members, and the format and type of information that needs to be provided.

Building a cross-functional team means that your MLOps development benefits from a broader skillset. The more your team members understand the skills of the wider team, the more effectively they can work together.

  • Engineers should recognise that the most pressing concern for data scientists is prototyping, experimentation and algorithm performance evaluation.
  • Data scientists often need to learn more about software development practices, and the separation of environments such as development, staging and production.

Ultimately, the goal of a cross-functional team is to create a clear framework that takes models through the entire development and production process. This framework should be built into the CI/CD framework. Create a simple document and spend a session taking data scientists through the development process that you have chosen. When the team forms, recognise that it is one team and organise yourself accordingly. Backlogs and stand-ups should be owned by and include the whole team.

If you’d like to know more about building effective MLOps team structures and operationalising machine learning, download our recent playbook, which is packed with insights into building successful MLOps projects and getting them into production.