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Introduction

Following a broad market trend toward the modernization of data science and machine learning (ML) as a fully integrated enterprise IT service, Google has delivered a new Jupyter Notebook development environment, Google Cloud AI Platform Notebooks.

Highlights

  • Google is no stranger to data science. But it s work in equipping data scientists with development tools has mostly gone unnoticed thanks to a largely indirect approach centered on scientific researchers with freely available Google Colab and on data enthusiasts with the wildly popular but independent platform Kaggle.

Features and Benefits

  • Analyzes Google Cloud AI Platform Notebooks’ approach to tackling JupyterLab-based machine learning software development.
  • Evaluates Google Cloud AI Platform Notebooks' effort to include access to Google’s portfolio of AI hardware accelerators.

Key questions answered

  • Does Google’s new JupyterLab-based notebook environment successfully meld DevOps methodologies with open source data science technologies?
  • How has Google integrated direct support for the popular software development versioning tool, GitHub?

Table of contents

Omdia view

  • Summary
  • Background
  • Key findings
  • Future developments

Appendix

  • Further reading
  • Author