Who Can Truly Fix Post-Deployment Issues with ML Models?

You’ve deployed your machine learning (ML) model. It’s running smoothly—until it isn’t. Predictions begin to drift, and business outcomes start missing the mark. Now what? Who can step in and truly fix these post-deployment issues?

The common assumption might be that Machine Learning Engineers are the go-to solution. After all, they’re the technical experts, the ones who built and deployed the model. They certainly understand the underlying algorithms and can debug code. However, their focus is typically on the tech stack—optimizing pipelines, scaling infrastructure, and ensuring models are up and running. What they often lack is the deeper business context to understand why the model is missing its goals or how market shifts might impact the model’s relevance.

On the other side, business stakeholders—executives, product owners, and managers—know the company’s strategic goals inside and out. They understand the KPIs and the long-term vision for how AI and ML fit into that strategy. But when model failures arise, they lack the technical depth to diagnose the root cause, recalibrate hyperparameters, or understand complex technical constraints like model drift.

So, when your model's predictions go off course, neither of these groups can fully bridge the gap.

The real fix comes from Data Scientists.

Data scientists are uniquely positioned to troubleshoot post-deployment issues. They have the rare combination of understanding the business objectives and the technical intricacies of the model. They can dive into the data to identify why the model is drifting, assess whether the training data still reflects the current environment, and adjust the model to better align with evolving business needs.

Here’s why Data Scientists are the key:

• Business Context: They’re involved early in the model-building phase, so they know what success looks like and how the model should align with the business goals.
• Model Knowledge: They designed the model, tuned it, and are intimately familiar with its weaknesses and strengths.
• Adaptability: Data scientists can adjust the model, re-train it, or even rebuild it entirely if the underlying assumptions no longer hold.

In a changing business landscape, models will inevitably encounter challenges post-deployment. Whether it’s market shifts, changes in customer behavior, or new data sources, Data Scientists are the ones who can connect the dots between business strategy and machine learning implementation.

Without that critical mix of technical expertise and business awareness, your model is likely to continue stumbling, or worse—fail entirely. So when things start to drift, it’s the Data Scientists who truly have the power to fix it.