So I considered integrating these new techniques and tools with what I’ve learnt so far through designing and developing “classical” enterprise level applications, by adapting the usual software development pipeline and tools to the specific case where machine learning models are involved..As a last stage, the run phase executes the application or service’s main process(es) in the target environment.This strict separation of stages implies for instance that it is forbidden to make changes to the application code directly in the runtime environment, but a new release cycle must be initiated to change source code, produce a new bundle and execute this on a target runtime as a new version..This guarantees many nice properties such as traceability, reproducibility of the process, and so on and so forth.All this consequently leads to set up separated (at least conceptually) runtime environments, where different stages are supported by different specific environments, each of which is equipped with the resource level, software infrastructure tools best fitted to support the specific activities related to the stage at issue.A development environment will need some IDE (like Eclipse, Visual Studio or PyCharm) and language runtime that allows developers to produce their code, a Continuous Integration (CI) environment will contain tools to share, version and manage both source code and compiled component binaries (eg. GIT, Maven, Jenkins, etc.), and finally the target runtime environment should be equipped with some a solid runtime infrastructure that allows the execution of the software comoponents while managing issues like distribution, load balancing, fault tolerance, and so on (lately I’m getting passionate with Kubernetes).Now, one of the first aspects that come to mind when considering machine learning projects is that the build phase gets conceptually slightly more complicated.. More details