IMO, this article misses the essence of and principles of MLOps. The essence of MLOps is that it is about processes (and tooling/platforms) for creating ML asse

IMO, this article misses the essence of and principles of MLOps. The essence of MLOps is that it is about processes (and tooling/platforms) for creating ML assets - features/labels and models. We call them FTI pipelines. Data engineering has data pipelines that produce datasets for consumption. In MLOps, feature pipelines produce features (from raw data). Training pipelines produce models (from features/labels). Inference pipelines produce predictions (from models + features). There is no such thing as a "ML Pipeline" in a production ML system. There is no ML pipeline that goes from raw data to predictions. We have the above FTI pipelines (feature/training/inference pipelines). The principles of MLOps are around being able to develop faster (shorten the development lifecyle) through automated testing and versioning. You need to validate data to build features. You need tested features to build models. You need to test models for your ML systems. It's a hierarchy: data->features->models-ML Apps. Versioning is needed for features and models in order to safely upgrade systems and enable them to evolve over time. I cover a lot of this in a course i developed called 'serverless ml'.