Summary
In this chapter, we’ve explored some of the common pitfalls in training and deploying ML models, including inadequate training data, poor data quality, over- and underfitting, training-serving skew, and model drift. We’ve also explored the concepts of bias and fairness, their impact on business outcomes, and how to mitigate these issues.
As we move forward, remember that data science is not just about building models, but also about ensuring that these models are reliable, fair, and beneficial to all stakeholders.
In the next chapter, we’ll explore the different types of data science projects you might encounter and how to approach each of them.
