Embarking on data science projects needs a robust methodology in planning the project, taking into consideration the potential scaling, maintenance, and team structure. We have learned how to define a business problem and quantify it with measurable parameters, so the next stage is a project plan that includes the development of the solution, to the deployment of a consumable business application.
This topic puts together some of the best industry practices structurally with examples for data science project life cycle management. This approach is an idealized sequence of stages; however, in real applications, the order can change according to the type of solution that is required.
Typically, a data science project for a single model deployment takes around three months, but this can increase to six months, or even up to a year. Defining a process from data to deployment is the key to reducing the time to deployment.