What is model deployment?
Model deployment is the process of integrating the machine learning model into a production environment. It allows using the trained model for business purposes and get actual practical results. It is one of the last stages in a machine learning workflow, and it usually requires tools and technologies outside of the machine learning field.
Why is it important?
Connecting machine learning and business is the primary purpose of data science. Data scientists are concerned with business problems that can be solved with machine learning. And to start using an ML model for business needs, it must be deployed into production. Without correct model deployment, a model is practically useless and cannot be used for decision-making processes.