Are you putting a trained inference model into production as a product? Is it a RL system (completely different architecture than an inference system)? Are you trying to build a model with your application data from scratch? Are you doing NLP or CV?
As a rule of thumb I look at the event diagram of the application/systems you're trying to implement ML into, which should tell you how to structure your data workflows in line with the existing data flows of the application. If it's strictly a constrained research effort then pipelines are less important, so go with what's fast and easy to document/read.
Generally speaking, you want your ML/DS/DE systems to be loosely coupled to your application data structures - with well defined RPC standards informed by the data team. I generally hate data pooling, but if we're talking about pooling 15 microservices vs pooling 15 monoliths, then the microservices pooling might be necessary.
Realistically this ends up being a business decision based on organizational complexity.
Are you putting a trained inference model into production as a product? Is it a RL system (completely different architecture than an inference system)? Are you trying to build a model with your application data from scratch? Are you doing NLP or CV?
As a rule of thumb I look at the event diagram of the application/systems you're trying to implement ML into, which should tell you how to structure your data workflows in line with the existing data flows of the application. If it's strictly a constrained research effort then pipelines are less important, so go with what's fast and easy to document/read.
Generally speaking, you want your ML/DS/DE systems to be loosely coupled to your application data structures - with well defined RPC standards informed by the data team. I generally hate data pooling, but if we're talking about pooling 15 microservices vs pooling 15 monoliths, then the microservices pooling might be necessary.
Realistically this ends up being a business decision based on organizational complexity.