AI pipelines powered by Jupyter notebooks
The Jupyter Notebook has become the de facto platform for data scientists and AI engineers to build interactive applications and develop AI/ML models. Luciano Resende details how to schedule related notebooks that correspond to different phases of the model lifecycle into notebook-based AI pipelines and walks you through scenarios that demonstrate how to reuse notebooks via parameterization.
| Talk Title | AI pipelines powered by Jupyter notebooks |
| Speakers | LUCIANO RESENDE (IBM) |
| Conference | O’Reilly Open Source Software Conference |
| Conf Tag | Fueling innovative software |
| Location | Portland, Oregon |
| Date | July 15-18, 2019 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
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. Luciano Resende details how to schedule together these 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.
