While the notebook is generally the domain of the data scientist, this book is intended for anyone who will be interacting at varying levels with the code, from developers and engineers who will be working with codebases and deployments, to management teams who wish to understand some of the issues facing the team.
We will provide examples in open source Jupyter notebooks, along with installation instructions for desktop-scale studies.
We will also provide examples in IBM Watson Studio, which is a cloud-hosted notebook environment that is also scalable. This service is free for smaller-scale studies, and it has a rich collection of examples that can be run with no installation requirements. This
environment also offers enterprise-grade collaboration tools for sharing code and data conveniently and securely.:
The book will cover:
- Using machine learning frameworks in a notebook