Sklearn pipeline objects provide an framework that simplifies the lifecycle of data science models. This talk will cover the how and why of encoding feature engineering, estimators, and model ensembles in a single deployable object.
Sklearn pipelines have advantages during both the development and deployment lifecycle of data science models. This talk will focus on what Sklearn pipelines are, how you benefit from using them in your workflow, and an example of a technical implementation (with some practical tips).
Some the advantages include: