Common challenges encountered in the ML model development life cycle

For some of the stages in the ML model development life cycle, we’ve already discussed various challenges that you are likely to encounter in those stages. However, in this section, we specifically call out major challenges that you need to be aware of as an AI/ML solutions architect interacting with companies who are implementing AI/ML workloads. In the Best practices for overcoming common challenges section later in this chapter, we’ll look at ways to overcome many of these challenges.

Finding and gathering relevant data

One of our first major challenges is finding relevant data for the business problem our models are being built to address. We presented some examples of potential data sources in the previous section, and in some cases, the data you need may be readily available to you, but finding the relevant data is not always straightforward for data scientists and data engineers. The following...