Often the outputs of data models developed by data, scientists end up in a report which summarizes the state of business and used by stakeholders to make decisions. But it is necessary to achieve a system that can predict the future outcomes in real time. This can be done by integrating the model in a production environment, however, it requires advance engineering skills and data scientists cannot do it alone. The process of deployment follows broadly 7 steps : 1.Refactor the model code
2. Walk through the code and determine how it slots into the engineering cycle
3.Re-write into a production stack language or PMML
4.Implement it into the tech stack
5. Test performance