The deployment of a machine learning (ML) model to production starts with actually building the model, which can be done in several ways and with many tools. The approach and tools used at the development stage are very important at ensuring the smooth integration of the basic units that make up the machine learning pipeline. If these are not put into consideration before starting a project, there’s a huge chance of you ending up with an ML system having low efficiency and high latency. For instance, using a function that has been deprecated might still work, but it tends to raise warnings and, as such, increases the response time of the system. The first thing to do in order to ensure this good integration of all system units is to have a system architecture (blueprint) that shows the end-to-end integration of each logical part in the system. Below is the designed system architecture for this mini-project. 链接地址： https://heartbeat.fritz.ai/deploying-machine-learning-models-on-google-cloud-platform-gcp-7b1ff8140144