3 Before you model: Planning and scoping a project
This chapter covers
- Defining effective planning strategies for ML project work
- Using efficient methods to evaluate potential solutions to an ML problem
The two biggest killers in the world of ML projects have nothing to do with what most data scientists ever imagine. These killers aren’t related to algorithms, data, or technical acumen. They have absolutely nothing to do with which platform you’re using, nor with the processing engine that will be optimizing a model. The biggest reasons for projects failing to meet the needs of a business are in the steps leading up to any of those technical aspects: the planning and scoping phases of a project.
Throughout most of the education and training ...
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