ML in der Logistitk (Praxisbeispiel)

Business understanding – What does the business need?

Data understanding – What data do we have / need? Is it clean?

Data preparation – How do we organize the data for modeling?

Modeling – What modeling techniques should we apply?

Evaluation – Which model best meets the business objectives?

Deployment – How do stakeholders access the results?