How do I begin a machine learning (ML) project?

Define a use case that matches the requirements of a ML project, identify aligned models, assess and prepare a dataset, design a training and testing process, and refine the model for best performance.

Once an organization reaches significant data science maturity, machine learning is a necessary tool within marketing to remain competitive and realize insights beyond traditional analytics. To begin a ML project, senior analysts or ML engineers define a use case aligned with company strategic goals, and determine if the data problem meets the requirements of a ML project: large relevant datasets, model types aligned with the data problem, and solution value justifying the project effort.



Once the use case is qualified as a potential ML project, lead analysts design the solution:

  • Identifying and validating datasets
  • Matching algorithm/model classes to the problem
  • Specify data prep requirements
  • Define training/testing approach


Defining the solution sets expectations, and allows detailed planning necessary to reach model deployment.


Learn more about machine learning in marketing here.