Integrating Data Science and MLOps: How to structure a collaboration and handoff process


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.

From this perspective, 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 different 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 throughout the ML lifecycle. Most importantly, I suggest locating both sides on the same team, and identify specific points in the workflow where collaboration is most beneficial.


Thomas is a senior machine learning engineer at a business and technology consulting firm, Logic2020, where he helps companies productionize ML models by adopting MLOps practices. He initially 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 integrates these diverse insights to build robust ML solutions.

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