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I had no idea how to build a Machine Learning Pipeline. But here’s what I figured.
Learn to build a Machine Learning Pipeline from scratch
When the going gets tough, the tough gets going.
As a postgraduate studying Artificial Intelligence (AI), my exposure to Machine Learning (ML) is largely academic. Yet, when given a task to create a simple ML pipeline for a time series forecast model, I realised how clueless I was. Also, I could barely find any specific information or code out there on this topic, hence I decided to write this topic.
This article will present a basic structure of how a simple ML pipeline can be created (More information may be supplemented over time).
What ARE Machine Learning pipelines and why are they relevant?
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.
