Machine Learning Pipelines - What Are They?

Most of MLOps concepts could be directly translated into how the office works. For example, machine learning pipeline is just an artifact. Only the data that is coming into the model is not. So you can test if we said it's fixed for rather than rate, right? Yes. There are a lot of data quality checks happening before and after predictions are being made. Data profiling is probably not what you would find in general software engineering. Feature stores make features shareable and discoverable. Second, they ensure that you only do feature engineering once - so you don't need to do it again.