Clarification of terminology in ML/MLOps
Increased interest in productionisation created a proliferation of terminology in ML/MLOps. This led to confusion and was recently raised at the MLOps Community slack. The following list is an attempt to clarify these:
ML - creating data defined "declarative" products as opposed to "imperative" software products. Instead of predefining rules, you need to find relationships straight from data.
MLOps - general paradigm around productionised ML (as opposed to academic ML, which only focuses on ML) to do ML in a business context and achieve positive ROI.
MLOps products - OSS or vendor-supplied tools to facilitate parts or whole of the MLOps paradigm.
MLOps architecture - Concrete implementation of infrastructure to facilitate MLOps made up of MLOps products (Buy) and custom implementations (Build).
MLOps processes - Concrete workflows practised on the MLOps architecture to achieve MLOps.
ML orchestration - All of the above: Selecting MLOps products, building MLOps architecture, then running the MLOps processes (but not the actual ML).
ML lifecycle management - High-level MLOps process regarding one model from start to finish.
ML Product Management - Making sure that everyone above is working on the right thing at the right time. Finding out what ML should or shouldn't be done and maintaining the ML lifecycle management.
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