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
6. Tweak the model based on test results
7.Slowly roll out the model.
Today many companies are adopting tools to make this process faster to reap the benefit of data driven decision making.
Read more at : https://www.datascience.com/blog/navigating-the-pitfalls-of-model-deployment