I had no idea how to build a Machine Learning Pipeline. But here's what I figured.

As the word'pipeline' suggests, it is a series of steps chained together in the ML cycle that often involves obtaining the data, processing the data, training/testing on various ML algorithms and finally obtaining some output (in the form of a prediction, etc). Unlike a traditional'pipeline', new real-life inputs and its outputs often feed back to the pipeline which updates the model. This article by Microsoft Azure describes ML pipelines well. Simply put, ML has become so widespread so quickly that accuracy of models has become equally important as the ability to access, scale and store these models. A ML pipeline is essentially an automated ML workflow. While they represent a fast and efficient way for data teams to build and deploy, this article does not address these aforementioned services.)