Applying data science and machine learning in marketing would be all glamorous and flawless if we were able to just build and use various machine learning models for different marketing use cases. However, that normally is not the case. Quite often, the end-to-end machine learning model building process can be tedious, with lots of barriers and bottlenecks on the way. We are going to discuss some of the most frequently appearing data science challenges in real life, including the following:
- Challenges in data
- Challenges in infrastructure
- Challenges in choosing the right model
