What strategies can you use to mitigate risks in an ML project?
Machine learning (ML) projects can be challenging and complex, involving multiple stages, stakeholders, and uncertainties. As an ML practitioner, you need to be aware of the potential risks that can affect the quality, performance, and impact of your ML solutions, and how to mitigate them. In this article, we will discuss some strategies that can help you reduce the risks in an ML project, from defining the problem and scope, to designing and testing the model, to deploying and monitoring the solution.