A Tale of 2 Cultures: Collaboration Between Data Scientists and MLOps Engineers

A Tale of 2 Cultures: How to structure the collaboration and handoff process between data scientists and MLOps engineers

While there has been immense progress in developing increasingly powerful ML and AI models, many organizations still struggle with productionalizing even basic models. Most of the recent progress in how to reliably operate ML models in production has come from the emerging field of MLOps. However, this is giving rise to the new challenge of how to integrate MLOps into the traditional data science workflow.
This presentation starts by framing the problem as an inherent conflict that arises from the fundamentally different needs of the explorative and interactive workflow in data science, compared to the engineering mindset required to manage the complexity of software systems running in production.
Thus, it becomes clear that this dilemma can’t simply be solved by imposing a common set of best practices. Instead, we need to define a separate set of quality standards for each side, and then find a good process for handing off work from data scientists to MLOps engineers.
There are three main categories of work that need to be handed over: code, models, and data. For each, I discuss the specific challenges involved, and suggest concrete strategies to overcome these.
The final section delves into general recommendations for structuring a successful handoff process. A particular focus is on how to reduce the gap between data scientists and MLOps engineers in the first place by building in mutual collaboration. Most importantly, I suggest locating both sides on the same team, and identify specific points in the workflow where collaboration is most beneficial.

About Our Speaker:
Thomas Loeber is a senior machine learning engineer at GoHealth, where he builds and productionizes GenAI models. Previously, he worked in consulting and at a technology startup, focusing on MLOps adoption. He originally came from the statistics and data science side, but has also worked in software and data engineering, searching for lessons from these more mature disciplines for how to create maintainable and scalable software systems. Now, Thomas is passionate about integrating these diverse insights to build robust ML systems.