1 – Identify the business needs and all the constraints and requirements
(what do we want to predict? what technical environment? what timing?)
=> clear and measurable objectives must be set with the project owner
2 – Manage the availability of data and perform an analysis
=> the data must be made easily accessible
=> their main characteristics must be understood
3 – Based on the analysis, perform the necessary transformations to make the data compatible with the requirements of the algorithms to be used
4 – Select several machine learning models compatible with the data, objectives and constraints.
5 – Train each model and evaluate its performance
=> compare the performances and select the most efficient model
6 – Deploy the model and use it in production
=> always listen to the business teams and regularly re-evaluate the model’s performance
