Читать книгу Machine Learning with Dynamics 365 and Power Platform - Vinnie Bansal - Страница 33

The last step of the machine learning lifecycle is deployment, where we deploy the ML model in the real‐world system. Deployment is a very crucial step in the machine learning lifecycle process. Deployment is a process of making your model available to make predictions in the production environment. The aim of this stage is to check the proper functionality of the model post‐deployment. The models need to be deployed in such a way that they can be used for inference as well as be updated regularly. If the prepared model produces an accurate result as per the specified requirements, with acceptable speed, only then do we deploy the model in the real system. But before deploying the project, you need to check whether it is improving its performance using available data or not and whether you want to go with a Platform as a Service (PaaS) or Infrastructure as a Service (IaaS). A PaaS is excellent for prototyping and businesses with lower traffic. Eventually, when the business grows and traffic increases, you need to switch to IaaS. This is the step to test the ability to predict outcomes in the real world.