Some researchers will want to write their own C3 package and leverage more of the AI Suite. C3 allows researchers to define their own types and methods to integrate their data into the C3 AI Suite along side the Datalake. This allows researchers to use C3 data analytics methods such as timeseries metrics just as they would on other Datalake data. Researchers will also have the ability to share their data with other researchers in the DTI by sharing their package. Adding another researcher's package as a dependency to your package will also bring another researcher's data into your package as well.

Pros:

• Define custom types and integrate new data into the C3 Datalake.
• Share data and methods through C3 packages.

Cons:

• C3 credentials and tenant/tag are required.

Getting Started

Training Curriculum

• C3 Provisioning Guide
  • Describes how to provision new code to a C3 environment
• DTI Provisioning Guide
• C3 Getting Started Guide
  • Not all sections of the Getting Started Guide are necessary.
    We list the necessary sections here:
    • Introduction to C3 AI Suite

    • Architecture

    • C3 Developer Console
    • Data Integration
    • Methods
    • Time Series Data Section
    • Metrics Section
    • Data Science
• DTI Guide to setup C3 lightbulb Package
  • C3 offers a training package called 'lightbulbExercise'. We offer a guide to help set this package up to allow the user to run C3 training notebooks.
  • The following C3 training notebooks should be
• DTI Data Integration Exercise (In Development)
  
  • A simplified version of the official C3 training Example lightbulbExercise.

Optional Resources/Training:

• DTI Guide to utilizing C3 Documentation
• DTI C3 Tips and Tricks

Worked Examples:

• Phylogenetic Tree building example (C3 Package) (PLANNED)
• mnist Example (C3 Package) (PLANNED)
• House Coverage Example (C3 Package) (PLANNED)