Building a Robust Machine Learning Pipeline: Best Practices and Common Pitfalls
Building a Robust Machine Learning Pipeline: Best Practices and Common Pitfalls
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In real life, the machine learning model is not a standalone object that only produces a prediction. It is part of an extended system that can only provide values if we manage it together. We need the machine learning (ML) pipeline to operate the model and deliver value.
Building an ML pipeline would require us to understand the end-to-end process of the machine learning lifecycle. This basic lifecycle includes data collection, preprocessing, model training, validation, deployment, and monitoring. In addition to these processes, the