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|Publication Date:||3Q 2020|
Google has delivered its Google Cloud AI Platform Notebooks, featuring close ties to other Google Cloud Platform (GCP) services and built-in open-source frameworks. The company is following a broad market trend toward the modernization of data science and machine learning (ML) as a fully integrated enterprise IT service. Omdia takes an in-depth look at the new platform, which gives Google a much needed unifying experience for what is otherwise a highly distributed set of AI development services.
Key Questions Addressed:
- Does Google’s new JupyterLab-based notebook environment successfully meld DevOps methodologies with open-source data science technologies?
- Which data science user role is Google targeting with Google Cloud AI Platform Notebooks?
- How does Google Cloud AI Platform Notebooks tackle JupyterLab-based ML software development requirements?
- How is Google integrating GitHub into Google Cloud AI Platform Notebooks as a means of versioning data science experiments?
- Does Google Cloud AI Platform Notebooks provide direct access to Google’s portfolio of AI services beyond hardware accelerators?
Who Needs This Report?
- Public cloud service providers
- Private and managed cloud data center solution providers
- Enterprises building AI and ML solutions
- MLOps solution vendors and service providers
- Investor community
Table of Contents
List of Charts, Figures, and Tables
- Basic Google Cloud AI Platform Notebooks interface with imported libraries
- GPU selection options
- GitHub integration
- Google What-If Tool