Description
The Jupyter Notebook has become the de-facto platform used by data scientists to 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. This session will detail different approaches to compose notebook based AI pipelines and running in different runtimes