The Jupyter Notebook has become the de facto platform used by data scientists and AI engineers to build interactive applications and develop their AI/ML models. In this scenario, it’s very common to decompose various phases of the development into multiple notebooks to simplify the development and management of the model lifecycle.

We will detail how to compose multiple notebooks that correspond to different phases of the model lifecycle into notebook-based AI pipelines and walk you through scenarios that demonstrate how to reuse notebooks via parameterization.